System and method for coordination of acceleration values of locomotives in a train consist

ABSTRACT

A train control system includes independent virtual in-train forces modelling engines onboard each of a plurality of locomotives in a train. Each of the plurality of locomotives may also include an analytics engine and a calibration engine configured to assimilate, analyze, and calibrate real time information from other locomotives and from draft gears and couplers interconnecting the locomotives with determinations made by the independent virtual in-train forces modelling engine onboard the respective locomotive, with the plurality of locomotives of the train being configured to operate collectively and coordinate their own acceleration values based on a common goal of minimizing in-train forces without being dependent on a command from a lead locomotive or central command.

TECHNICAL FIELD

The present disclosure relates generally to a system and method fortrain control and, more particularly, a system and method for usingindependent, intra-consist, in-train forces models generated on each ofa plurality of locomotives of a train for coordinating accelerationvalues of each of the locomotives to control in-train forces in thetrain.

BACKGROUND

Trains may include multiple powered units, such as locomotives, that aremechanically coupled or linked together in a consist. The consist oflocomotives operates to provide tractive and/or braking efforts topropel and stop movement of the train. Each of the locomotives in theconsist may change the supplied tractive and/or braking efforts, basedon a data message that is communicated to the locomotive, or based atleast in part on commands generated by an onboard energy managementsystem in conjunction with information that may be received from otherlocomotives or offboard sources of information. In some embodiments, thesupplied tractive and/or braking efforts may be based at least in parton Positive Train Control (PTC) instructions or control information foran upcoming trip. The control information may be used by a softwareapplication to determine the speed of the train for various segments ofan upcoming trip of the rail vehicle. Control systems and subsystems forcontrolling and monitoring the tractive and/or braking efforts performedby one or more locomotives of the train and performing other operationsassociated with the locomotives and other rail cars in a train may belocated in part on one or more of the powered units and/or distributedacross one or more servers off-board the vehicle at one or more remotecontrol stations.

A goal in the operation of the locomotives and other rail cars in atrain is to provide the most accurate and up-to-date informationregarding operational characteristics of the entire train andcoordination of acceleration values for each of the locomotives in orderto maintain in-train forces within desired limits regardless of thenumber of locomotives and rail cars in the train or the terrain overwhich the train is traveling, while optimizing fuel efficiency andenabling and enhancing on-time performance. In order to achieve thegoal, e.g., of providing automatic train operation (ATO), a reliable,precisely calibrated and synchronized computerized control system mustbe provided in order to generate and transmit train control commands andother data indicative of operational characteristics associated with thevarious computer systems and subsystems of the locomotive consists andother rail cars between the train and an off-board, remote controllerinterface (also sometimes referred to as the “back office”). The controlsystem must be capable of transmitting data messages having theinformation used to control the tractive and/or braking efforts of therail vehicle and other operational characteristics of the variousconsist subsystems while the rail vehicle is moving. The control systemmust also be able to transmit information regarding a detected faulton-board a locomotive, and possibly respond with control commands toreset the fault. There is also a need for a train tracking andmonitoring system that determines and presents current, real-timeposition information for one or more trains in a railroad network, theconfiguration or arrangement of powered and non-powered units withineach of the trains, and operational status of the various systems andsubsystems of the trains that are being tracked. Advances in thebandwidth, throughput, data transmission speeds, and other capabilitiesof various telecommunication networks, including 5G wirelesscommunication networks, enables the placement of a large number ofsensor devices throughout the train, and communication of sensor data toand from various control systems and subsystems of the trains. Thecontrol systems and subsystems may be distributed locally on leading andtrailing consists of the trains, and/or remotely, off-board the trainsat one or more distributed remote servers or control centers such as theback office and other control centers connected over the Internet orother communication networks. Proper synchronization, calibration, andcoordination between the distributed control systems is important fordetermining the exact configuration of the train and operational statusof all train assets, systems, and subsystems at any point in time, andimplementing reconfiguration of train assets and/or changes inoperational parameters of the systems and subsystems when necessary tomeet operational goals.

One example of a powered system, such as a train, that includes acontrol system for remotely controlling speed regulation of the poweredsystem to improve efficiency of operation of the powered system isdisclosed in U.S. Pat. No. 8,989,917 of Kumar, that issued on Mar. 24,2015 (“the ‘917 patent”). In particular, the ‘917 patent discloses asystem for operating a remotely controlled powered system. The systemincludes feedforward and feedback elements configured to provide andreceive information related to predicted and actual movement of thepowered system to remotely control the speed of the system to improveefficiency of operation.

Although useful in allowing for remote control of the speed of operationof one or more locomotives in a train, the system of the ‘917 patent maybe limited. In particular, the ‘917 patent does not provide a traincontrol system that enables each one of the powered units or locomotivesin the train to independently determine the optimal acceleration valuesfor the respective locomotive, while assimilating information receivedfrom other locomotives with outputs from a virtual system in-trainforces modelling engine onboard the locomotive, but without requiring acommand from a lead locomotive or offboard back office or other centralserver. The ability to independently determine and control optimalacceleration values at each of the locomotives or powered units on atrain, while synchronizing this energy management with the otherlocomotives or powered units of the train, for controlling in-trainforces and optimizing other operational characteristics, would alleviateproblems caused by a potential breakdown in communications with a leadlocomotive or central control command.

The present disclosure is directed at overcoming one or more of theshortcomings set forth above and/or other problems of the prior art.

SUMMARY

In one aspect, the present disclosure is directed to a train controlsystem comprising independent virtual in-train forces modelling enginesonboard each of a plurality of locomotives in a train. Each of theplurality of locomotives also includes an analytics engine and acalibration engine configured to assimilate, analyze, and calibrate realtime information from other locomotives and from sensors disposed ondraft gears and couplers interconnecting the locomotives withdeterminations made by the independent virtual in-train forces modellingengine onboard the respective locomotive, with the plurality oflocomotives of the train being configured to operate collectively basedon a common goal of minimizing in-train forces without being dependenton a command from a lead locomotive or central command. Each of theindependent virtual in-train forces modelling engines is configured touse machine learning for controlling the acceleration values for thelocomotive on which it is mounted independently from any commandreceived from offboard the locomotive. The train control system includesa data acquisition hub communicatively connected to one or more ofsensors and databases associated with one or more locomotives or othercomponents of a train and configured to acquire real-time and historicaloperational and structural data from one or more of systems andcomponents of the train. The one or more sensors acquiring real-timedata include one or more of LIDAR sensors, RADAR sensors,accelerometers, gyroscopic sensors, optical recognition sensors, andphysical strain gages configured to measure displacements or forces atthe draft gears and couplers interconnecting the locomotives. Each ofthe independent virtual in-train forces modelling engines mounted on arespective locomotive may be configured to simulate in-train forces andtrain operational characteristics using physics-based equations,kinematic or dynamic modeling of behavior of the train or components ofthe train during operation when the train is accelerating or slowingdown, and inputs derived from stored historical contextual datacomprising one or more of a number of locomotives in the train, positionof the locomotive in the train, age or amount of usage of one or morelocomotives of the train or other components of the train, weightdistribution of the train, length of the train, speed of the train,control configurations for one or more locomotives or consists of thetrain, power notch settings of one or more locomotives of the train,braking implemented in the train, positive train control characteristicsimplemented in the train, grade, topology, temperature, or othercharacteristics of train tracks on which the train is operating, andengine operational parameters that affect performance of one or morelocomotive engines for the train. A virtual system model database may beconfigured to store one or more virtual system models simulated by theindependent virtual in-train forces modelling engine, wherein each ofthe one or more virtual system models includes a mapping betweendifferent combinations of the stored historical contextual data andcorresponding simulated in-train forces and train operationalcharacteristics that occur when the train is accelerating or slowingdown. The train control system may also include an energy managementsystem associated with each of the plurality of locomotives of the trainand configured to adjust one or more of throttle requests, dynamicbraking requests, and pneumatic braking requests for each of thelocomotives based at least in part on real time information from otherlocomotives and draft gears and couplers interconnecting the locomotivesassimilated with determinations made by the independent virtual in-trainforces modelling engine onboard the respective locomotive, with theplurality of locomotives of the train being configured to operatecollectively based on a common goal of minimizing in-train forces.

In another aspect, the present disclosure is directed to a method ofusing independent virtual in-train forces modelling engines onboard eachof a plurality of locomotives in a train to coordinate the accelerationof each of the locomotives to minimize in-train forces without beingdependent on a command from a lead locomotive or central command. Themethod may include assimilating, analyzing, and calibrating real timeinformation from other locomotives and draft gears and couplersinterconnecting the locomotives with determinations made by theindependent virtual in-train forces modelling engine onboard therespective locomotive, and operating the plurality of locomotives of thetrain collectively based on a common goal of minimizing in-train forceswithout being dependent on a command from a lead locomotive or centralcommand. The method may include each of the independent virtual in-trainforces modelling engines using machine learning for controlling theacceleration values for the locomotive on which it is mountedindependently from any command received from offboard the locomotive. Adata acquisition hub communicatively connected to one or more of sensorsand databases associated with one or more locomotives or othercomponents of a train may acquire real-time and historical operationaland structural data from one or more of systems and components of thetrain. The one or more sensors acquiring real-time data may include oneor more of LIDAR sensors, RADAR sensors, accelerometers, gyroscopicsensors, optical recognition sensors, and physical strain gagesconfigured to measure displacements or forces at the draft gears andcouplers interconnecting the locomotives. Each of the independentvirtual in-train forces modelling engines mounted on a respectivelocomotive may simulate in-train forces and train operationalcharacteristics using physics-based equations, kinematic or dynamicmodeling of behavior of the train or components of the train duringoperation when the train is accelerating or slowing down, and inputsderived from stored historical contextual data comprising one or more ofa number of locomotives in the train, position of the locomotive in thetrain, age or amount of usage of one or more locomotives of the train orother components of the train, weight distribution of the train, lengthof the train, speed of the train, control configurations for one or morelocomotives or consists of the train, power notch settings of one ormore locomotives of the train, braking implemented in the train,positive train control characteristics implemented in the train, grade,topology, temperature, or other characteristics of train tracks on whichthe train is operating, and engine operational parameters that affectperformance of one or more locomotive engines for the train. One or morevirtual system models simulated by the independent virtual in-trainforces modelling engine may be stored in a virtual system modeldatabase, with each of the one or more virtual system models including amapping between different combinations of the stored historicalcontextual data and corresponding simulated in-train forces and trainoperational characteristics that occur when the train is accelerating orslowing down. An energy management system onboard each of thelocomotives of the train may adjust one or more of throttle requests,dynamic braking requests, and pneumatic braking requests for each of thelocomotives based at least in part on real time information from otherlocomotives and draft gears and couplers interconnecting the locomotivesassimilated with determinations made by the independent virtual in-trainforces modelling engine onboard the respective locomotive, with theplurality of locomotives of the train operating collectively based on acommon goal of minimizing in-train forces.

In yet another aspect, the present disclosure is directed to alocomotive for a train, wherein the locomotive includes a locomotivecontrol system that includes a learning system onboard the locomotive.The learning system may be configured to receive real-time andhistorical operational and structural data for use as training data fromone or more systems or components of the train at a data acquisition hubcommunicatively connected to one or more of sensors and databasesassociated with one or more locomotives or other components of a train.The learning system may simulate, using a virtual system modelingengine, in-train forces and train operational characteristics usingphysics-based equations, kinematic or dynamic modeling of behavior ofthe train or components of the train during operation when the train isaccelerating or slowing down, and inputs derived from stored historicalcontextual data comprising one or more of a number of locomotives in thetrain, age or amount of usage of one or more locomotives of the train orother components of the train, weight distribution of the train, lengthof the train, speed of the train, control configurations for one or morelocomotives or consists of the train, power notch settings of one ormore locomotives of the train, braking implemented in the train,positive train control characteristics implemented in the train, grade,temperature, or other characteristics of train tracks on which the trainis operating, and engine operational parameters that affect performanceof one or more locomotive engines for the train. The learning system mayalso be configured to store one or more virtual system models simulatedby the virtual system modeling engine in a virtual system modeldatabase, wherein each of the one or more virtual system models includesa mapping between different combinations of the stored historicalcontextual data and corresponding simulated in-train forces and trainoperational characteristics that occur when the train is accelerating orslowing down. The learning system may calculate, using a machinelearning engine, relative weights to assign to each of different typesof the stored historical contextual data of each of the one or morevirtual system models and assign the relative weights to the storedhistorical contextual data. The learning system may also be trained bythe machine learning engine using the weighted stored historicalcontextual data and the training data to determine a probability of eachof the one or more virtual system models providing an accuraterepresentation of actual in-train forces and train operationalcharacteristics that occur during acceleration and slowing down of thetrain. The machine learning engine may use a learning function includingat least one learning parameter. Training the learning system mayinclude providing the weighted stored historical contextual data as aninput to the learning function, the learning function being configuredto use the at least one learning parameter to generate an output basedon the input. Training may also include causing the learning function togenerate the output based on the input, comparing the output to thetraining data, wherein the training data includes data produced bysensors having captured actual information on in-train forces and trainoperational characteristics during acceleration and slowing down of thetrain. The sensors may include one or more of LIDAR sensors, RADARsensors, accelerometers, gyroscopic sensors, optical recognitionsensors, and physical strain gages configured to measure displacementsor forces at draft gears and couplers interconnecting the locomotivewith another locomotive or rail car. The learning system of thelocomotive control system may also be configured to compare thedetermined probabilities of each of the virtual system models to apredetermined threshold probability level, and initiate adjustments toone or more of the calculated relative weights assigned to each of thedifferent types of the stored historical contextual data of each of theone or more virtual system models to improve the determinedprobabilities of each of the virtual system models based on actualinformation on in-train forces and train operational characteristicsacquired from a plurality of different trains operating under differentconditions. The learning system may use an energy management systemassociated with the one or more locomotives of the train to adjust oneor more of throttle requests, dynamic braking requests, and pneumaticbraking requests for the one or more locomotives of the train based atleast in part on one of the virtual system models with the highestprobability of providing an accurate representation of actual in-trainforces and train operational characteristics and with one or more of thesimulated in-train forces and train operational characteristics fallingwithin a predetermined acceptable range of values.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of one embodiment of a control system fora train;

FIG. 2 is a block diagram of one implementation of a portion of thecontrol system illustrated in FIG. 1 ;

FIG. 3 is an illustration of a system for utilizing real-time data forpredictive analysis of the performance of a monitored system, inaccordance with one embodiment of the disclosure;

FIG. 4 is a schematic diagram of independent power control theoryaccording to an embodiment of this disclosure;

FIG. 5 is a schematic diagram of an exemplary intra-consist force modelbetween consists;

FIG. 6 is a schematic diagram representative of the collectiveassimilation of independently measured draft gear forces and virtualintra-consist modeled forces at each of a plurality of locomotives in aconsist;

FIG. 7 is a block diagram of one implementation of draft gear forcemeasurement; and

FIG. 8 is a schematic diagram representative of buff and draft forcesthat may be measured at each of the draft gears interconnectinglocomotives in a train.

DETAILED DESCRIPTION

FIG. 1 is a schematic diagram of one embodiment of a control system 100for operating a train 102 traveling along a track 106. The train mayinclude multiple rail cars (including powered and/or non-powered railcars or units) linked together as one or more consists or a single railcar (a powered or non-powered rail car or unit). The control system 100may provide for cost savings, improved safety, increased reliability,operational flexibility, and convenience in the control of the train 102through communication of network data between an off-board remotecontroller interface 104 and the train 102. The control system 100 mayalso provide a means for remote operators or third party operators tocommunicate with the various locomotives or other powered units of thetrain 102 from remote interfaces that may include any computing deviceconnected to the Internet or other wide area or local communicationsnetwork. The control system 100 may be used to convey a variety ofnetwork data and command and control signals in the form of messagescommunicated to the train 102, such as packetized data or informationthat is communicated in data packets, from the off-board remotecontroller interface 104. The off-board remote controller interface 104may also be configured to receive remote alerts and other data from acontroller on-board the train, and forward those alerts and data todesired parties via pagers, mobile telephone, email, and online screenalerts. The data communicated between the train 102 and the off-boardremote controller interface 104 may include signals indicative ofvarious operational parameters associated with components and subsystemsof the train, signals indicative of fault conditions, signals indicativeof maintenance activities or procedures, and command and control signalsoperative to change the state of various circuit breakers, throttles,brake controls, actuators, switches, handles, relays, and otherelectronically-controllable devices on-board any locomotive or otherpowered unit of the train 102. The remote controller interface 104 alsoenables the distribution of the various computer systems such as controlsystems and subsystems involved in operation of the train or monitoringof train operational characteristics at one or more remote locationsoff-board the train and accessible by authorized personnel over theInternet, wireless telecommunication networks, and by other means. Invarious exemplary embodiments, a centralized or cloud-based computerprocessing system may be located in one or more of a back-office serveror a plurality of servers remote from the train. One or moredistributed, edge-based computer processing systems may be locatedon-board one or more locomotives of the train, and each of thedistributed computer processing systems may be communicatively connectedto the centralized computer processing system.

The control system 100 may also include independent virtual in-trainforces modelling engines onboard each of a plurality of locomotives intrain 102. Each of the plurality of locomotives may also include ananalytics engine and a calibration engine configured to assimilate,analyze, and calibrate real time information from other locomotives andfrom sensors associated with draft gears and couplers interconnectingthe locomotives with determinations made by the independent virtualin-train forces modelling engine onboard the respective locomotive, withthe plurality of locomotives of the train being configured to operatecollectively based on a common goal of minimizing in-train forceswithout being dependent on a command from a lead locomotive or centralcommand. Each of the independent virtual in-train forces modellingengines may be configured to use machine learning for controlling theacceleration values for the locomotive on which it is mountedindependently from any command received from offboard the locomotive.The train control system may also include a data acquisition hubcommunicatively connected to one or more of sensors and databasesassociated with one or more locomotives or other components of a trainand configured to acquire real-time and historical operational andstructural data from one or more of systems and components of the train.The one or more sensors acquiring real-time data may include one or moreof LIDAR sensors, RADAR sensors, accelerometers, gyroscopic sensors,optical recognition sensors, and physical strain gages configured tomeasure displacements or forces at the draft gears and couplersinterconnecting the locomotives. Each of the independent virtualin-train forces modelling engines mounted on a respective locomotive maybe configured to simulate in-train forces and train operationalcharacteristics using physics-based equations, kinematic or dynamicmodeling of behavior of the train or components of the train duringoperation when the train is accelerating or slowing down, and inputsderived from stored historical contextual data comprising one or more ofa number of locomotives in the train, position of the locomotive in thetrain, age or amount of usage of one or more locomotives of the train orother components of the train, weight distribution of the train, lengthof the train, speed of the train, control configurations for one or morelocomotives or consists of the train, power notch settings of one ormore locomotives of the train, braking implemented in the train,positive train control characteristics implemented in the train, grade,topology, temperature, or other characteristics of train tracks on whichthe train is operating, and engine operational parameters that affectperformance of one or more locomotive engines for the train. A virtualsystem model database may be configured to store one or more virtualsystem models simulated by the independent virtual in-train forcesmodelling engine, wherein each of the one or more virtual system modelsincludes a mapping between different combinations of the storedhistorical contextual data and corresponding simulated in-train forcesand train operational characteristics that occur when the train isaccelerating or slowing down. The train control system may also includean energy management system associated with each of the plurality oflocomotives of the train and configured to adjust one or more ofthrottle requests, dynamic braking requests, and pneumatic brakingrequests for each of the locomotives based at least in part on real timeinformation from other locomotives and sensor data from draft gears andcouplers interconnecting the locomotives assimilated with determinationsmade by the independent virtual in-train forces modelling engine onboardthe respective locomotive, with the plurality of locomotives of thetrain being configured to operate collectively based on a common goal ofminimizing in-train forces.

Some control strategies undertaken by the control system 100 may includeasset protection provisions, whereby asset operations are automaticallyderated or otherwise reduced in order to protect train assets, such as alocomotive, or the draft gears or couplers interconnecting thelocomotives, from sustaining damage. For example, when the controlsystem detects via sensors that the coolant temperature, oiltemperature, crankcase pressure, or another operating parameterassociated with a locomotive has exceeded a threshold, the controlsystem may be configured to automatically reduce engine power (e.g., viaa throttle control) to allow the locomotive to continue the currentmission with a reduced probability of failure. In other circumstances,the control system may determine collectively as a result of theindependent modeling of in-train forces at each locomotive assimilatedwith measured forces at the draft gears interconnecting the locomotivesin a train that a maximum allowable number of locomotives and/or railcars has been reached or exceeded in the train for a particular traintrip. In addition to derating or otherwise reducing certain assetoperations based on threshold levels of operational parameters, assetprotection may also include reducing or stopping certain operationsbased on the number, frequency, or timing of maintenance operations orfaults detected by various sensors. In some cases, the control systemmay be configured to fully derate the propulsion systems of thelocomotive and/or bring the train 102 to a complete stop to preventdamage to the propulsion systems in response to signals generated bysensors. In this way, the control system may automatically exerciseasset protection provisions of its control strategy to reduce incidentsof debilitating failure and the costs of associated repairs.

At times, however, external factors may dictate that the train 102should continue to operate without an automatic reduction in enginepower, or without bringing the train to a complete stop. The costsassociated with failing to complete a mission on time can outweigh thecosts of repairing one or more components, equipment, subsystems, orsystems of a locomotive. In one example, a locomotive of the train maybe located near or within a geo-fence characterized by a track grade orother track conditions that require the train 102 to maintain a certainspeed and momentum in order to avoid excessive wheel slippage on thelocomotive, or even stoppage of the train on the grade, while keepingin-train forces within allowable parameters. Factors such as the trackgrade, environmental factors, and power generating capabilities of oneor more locomotives approaching or entering the pre-determined geo-fencemay result in an unacceptable delay if the train were to slow down orstop. In certain situations the train may not even be able to continueforward if enough momentum is lost, or in-train forces may be determinedby the virtual in-train forces modelling engines to potentially exceedallowable thresholds, resulting in considerable delays and expense whileadditional locomotives are moved to the area to get the train startedagain. In some implementations of this disclosure the geo-fences may becharacterized as no-stop zones, unfavorable-stop zones, orfavorable-stop zones.

In situations when a train is approaching a geo-fence characterized asone of the above-mentioned zones, managers of the train 102 may wish totemporarily modify or disable asset protection provisions associatedwith automatic control of the locomotive to allow the train 102 tocomplete its mission on time. The independent virtual in-train forcesmodelling engines mounted on each locomotive according to variousembodiments of this disclosure enable a determination of optimalacceleration values for each locomotive in order to minimize potentiallydamaging in-train forces without requiring the receipt of commands onthe locomotive from a lead locomotive or central command (back office).The control systems on-board each locomotive may be configured toassimilate information received from other locomotives and off-boardsources of information with the determined acceleration values generatedby the virtual in-train forces modelling engines. All of the locomotivesin a train configured according to embodiments of this disclosure maycontinue to operate collectively in order to minimize in-train forces,while not depending on any commands from a lead locomotive or centralcommand. However, managers having the responsibility or authority tomake operational decisions with such potentially costly implications maybe off-board the train 102 or away from a remote controller interface,such as at a back office or other network access point. To avoidunnecessary delays in reaching a decision to temporarily modify ordisable asset protection provisions of automatic train operation (ATO),the control system 100 may be configured to facilitate the selection ofride-through control levels via a user interface at an on-boardcontroller or at the off-board remote controller interface 104. Thecontrol system 100 may also be configured to generate a ride-throughcontrol command signal including information that may be used to directthe locomotive to a geo-fence with a more favorable stop zone.Additionally, control system 100, characterized by the collectiveoperations of independent virtual in-train forces modelling engines oneach of the locomotives with assimilated information received from otherlocomotives and off-board sources of information, may be configured forthe collective control of the acceleration values for each of thelocomotives of the train following braking, such as dynamic braking atthe bottom of one hill before then accelerating up an adjacent hill inthe direction the train is traveling along train tracks.

The off-board remote controller interface 104 may be connected with anantenna module 124 configured as a wireless transmitter or transceiverto wirelessly transmit data messages and control commands to the train102. The messages and commands may originate elsewhere, such as in arail-yard back office system, one or more remotely located servers (suchas in the “cloud”), a third party server, a computer disposed in arail-yard tower, and the like, and be communicated to the off-boardremote controller interface 104 by wired and/or wireless connections.Alternatively, the off-board remote controller interface 104 may be asatellite that transmits the messages and commands down to the train 102or a cellular tower disposed remote from the train 102 and the track106. Other devices may be used as the off-board remote controllerinterface 104 to wirelessly transmit the messages. For example, otherwayside equipment, base stations, or back office servers may be used asthe off-board remote controller interface 104. By way of example only,the off-board remote controller interface 104 may use one or more of theTransmission Control Protocol (TCP), Internet Protocol (IP), TCP/IP,User Datagram Protocol (UDP), or Internet Control Message Protocol(ICMP) to communicate network data over the Internet with the train 102.

As described below, the network data can include information used toautomatically and/or remotely control operations of the train 102 orsubsystems of the train, and/or reference information stored and used bythe train 102 during operation of the train 102. The network datacommunicated to the off-board remote controller interface 104 from thetrain 102 may also provide alerts and other operational information thatallows for remote monitoring, diagnostics, asset management, andtracking of the state of health of all of the primary power systems andauxiliary subsystems such as HVAC, air brakes, lights, event recorders,and the like. The increased use of distributed computer systemprocessing enabled by advances in network communications, including butnot limited to 5G wireless telecommunication networks, allows for theremote location of distributed computer system processors that mayperform intensive calculations and/or access large amounts of real-timeand historical data related to the train operational parameters. Thisdistributed computer system processing may also introduce potentialbreakdowns in communication or transient latency issues between thedistributed nodes of the communication network, leading to potentialsynchronization and calibration problems between various computercontrol systems and subsystems. The control system 100 and/or offboardremote control interface 104, according to various embodiments of thisdisclosure, may employ artificial intelligence algorithms and/or machinelearning engines or processing modules to train learning algorithmsand/or create virtual system models, such as the virtual in-train forcesmodels created by the independent virtual in-train forces modellingengines on each locomotive, and perform comparisons between real-timedata, historical data, and/or predicted data, to find indicators orpatterns in which the distributed computer systems may facesynchronization problems. The early identification of any potentialsynchronization or calibration problems between the various distributedcomputer systems or subsystems using machine learning and virtual systemmodels enables early implementation of proactive measures to mitigatethe problems.

The train 102 may include a lead consist 114 of powered locomotives,including the interconnected powered units 108 and 110, one or moreremote or trailing consists 140 of powered locomotives, includingpowered units 148, 150, and additional non-powered units 112, 152.“Powered units” refers to rail cars that are capable of self-propulsion,such as locomotives. “Non-powered units” refers to rail cars that areincapable of self-propulsion, but which may otherwise receive electricpower for other services. For example, freight cars, passenger cars, andother types of rail cars that do not propel themselves may be“non-powered units”, even though the cars may receive electric power forcooling, heating, communications, lighting, and other auxiliaryfunctions.

In the illustrated embodiment of FIG. 1 , the powered units 108, 110represent locomotives joined with each other in the lead consist 114.The lead consist 114 represents a group of two or more locomotives inthe train 102 that are mechanically coupled or linked together to travelalong a route. The lead consist 114 may be a subset of the train 102such that the lead consist 114 is included in the train 102 along withadditional trailing consists of locomotives, such as trailing consist140, and additional non-powered units 152, such as freight cars orpassenger cars. While the train 102 in FIG. 1 is shown with a leadconsist 114, and a trailing consist 140, alternatively the train 102 mayinclude other numbers of locomotive consists joined together orinterconnected by one or more intermediate powered or non-powered unitsthat do not form part of the lead and trailing locomotive consists.

The powered units 108, 110 of the lead consist 114 include a leadpowered unit 108, such as a lead locomotive, and one or more trailingpowered units 110, such as trailing locomotives. As used herein, theterms “lead” and “trailing” are designations of different powered units,and do not necessarily reflect positioning of the powered units 108, 110in the train 102 or the lead consist 114. For example, a lead poweredunit may be disposed between two trailing powered units. Alternatively,the term “lead” may refer to the first powered unit in the train 102,the first powered unit in the lead consist 114, and the first poweredunit in the trailing consist 140. The term “trailing” powered units mayrefer to powered units positioned after a lead powered unit. In anotherembodiment, the term “lead” refers to a powered unit that is designatedfor primary control of the lead consist 114 and/or the trailing consist140, and “trailing” refers to powered units that are under at leastpartial control of a lead powered unit.

The powered units 108, 110 include a connection at each end of thepowered unit 108, 110 to couple propulsion subsystems 116 of the poweredunits 108, 110 such that the powered units 108, 110 in the lead consist114 function together as a single tractive unit. The propulsionsubsystems 116 may include electric and/or mechanical devices andcomponents, such as diesel engines, electric generators, and tractionmotors, used to provide tractive effort that propels the powered units108, 110 and braking effort that slows the powered units 108, 110.

Similar to the lead consist 114, the embodiment shown in FIG. 1 alsoincludes the trailing consist 140, including a lead powered unit 148 anda trailing powered unit 150. The trailing consist 140 may be located ata rear end of the train 102, or at some intermediate point along thetrain 102. Non-powered units 112 may separate the lead consist 114 fromthe trailing consist 140, and additional non-powered units 152 may bepulled behind the trailing consist 140.

The propulsion subsystems 116 of the powered units 108, 110 in the leadconsist 114 may be connected and communicatively coupled with each otherby a network connection 118. In one embodiment, the network connection118 includes a net port and jumper cable that extends along the train102 and between the powered units 108, 110. The network connection 118may be a cable that includes twenty seven pins on each end that isreferred to as a multiple unit cable, or MU cable. Alternatively, adifferent wire, cable, or bus, or other communication medium, may beused as the network connection 118. For example, the network connection118 may represent an Electrically Controlled Pneumatic Brake line(ECPB), a fiber optic cable, or wireless connection - such as over a 5Gtelecommunication network. Similarly, the propulsion subsystems 156 ofthe powered units 148, 150 in the trailing consist 140 may be connectedand communicatively coupled to each other by the network connection 118,such as a MU cable extending between the powered units 148, 150, orwireless connections.

The network connection 118 may include several channels over whichnetwork data is communicated. Each channel may represent a differentpathway for the network data to be communicated. For example, differentchannels may be associated with different wires or busses of amulti-wire or multi-bus cable. Alternatively, the different channels mayrepresent different frequencies or ranges of frequencies over which thenetwork data is transmitted.

The powered units 108, 110 may include communication units 120, 126configured to communicate information used in the control operations ofvarious components and subsystems, such as the propulsion subsystems 116of the powered units 108, 110. The communication unit 120 disposed inthe lead powered unit 108 may be referred to as a lead communicationunit. The lead communication unit 120 may be the unit that initiates thetransmission of data packets forming a message to the off-board, remotecontroller interface 104. For example, the lead communication unit 120may transmit a message via a WiFi or cellular modem to the off-boardremote controller interface 104. The message may contain information onan operational state of the lead powered unit 108, such as a throttlesetting, a brake setting, readiness for dynamic braking, the tripping ofa circuit breaker on-board the lead powered unit, or other operationalcharacteristics. Additional operational information associated with alocomotive such as an amount of wheel slippage, wheel temperatures,wheel bearing temperatures, brake temperatures, and dragging equipmentdetection may also be communicated from sensors on-board a locomotive orother train asset, or from various sensors located in wayside equipmentor sleeper ties positioned at intervals along the train track. Thecommunication units 126 may be disposed in different trailing poweredunits 110 and may be referred to as trailing communication units.Alternatively, one or more of the communication units 120, 126 may bedisposed outside of the corresponding powered units 108, 110, such as ina nearby or adjacent non-powered unit 112. Another lead communicationunit 160 may be disposed in the lead powered unit 148 of the trailingconsist 140. The lead communication unit 160 of the trailing consist 140may be a unit that receives data packets forming a message transmittedby the off-board, remote controller interface 104. For example, the leadcommunication unit 160 of the trailing consist 140 may receive a messagefrom the off-board remote controller interface 104 providing operationalcommands that are based upon the information transmitted to theoff-board remote controller interface 104 via the lead communicationunit 120 of the lead powered unit 108 of the lead consist 114. Atrailing communication unit 166 may be disposed in a trailing poweredunit 150 of the trailing consist 140, and interconnected with the leadcommunication unit 160 via the network connection 118.

The communication units 120, 126 in the lead consist 114, and thecommunication units 160, 166 in the trailing consist 140 may beconnected with the network connection 118 such that all of thecommunication units for each consist are communicatively coupled witheach other by the network connection 118 and linked together in acomputer network. Alternatively, the communication units may be linkedby another wire, cable, or bus, or be linked by one or more wirelessconnections. As discussed above, various embodiments of this disclosuremay include control systems on-board each locomotive that are configuredto assimilate information received from other locomotives and off-boardsources of information with independently determined acceleration valuesgenerated by a virtual in-train forces modelling engine on-board thelocomotive. All of the locomotives in a train configured according toembodiments of this disclosure may continue to operate collectively inorder to minimize in-train forces, while not depending on any commandsfrom a lead locomotive or central command. Nonetheless, thecommunication units and wired or wireless networked connections betweenthe locomotives may provide additional backup avenues for exchanginginformation useful in modeling in-train forces and coordinatingcollective control operations of the locomotives.

The networked communication units 120, 126, 160, 166 may include antennamodules 122. The antenna modules 122 may represent separate individualantenna modules or sets of antenna modules disposed at differentlocations along the train 102. For example, an antenna module 122 mayrepresent a single wireless receiving device, such as a single 220 MHzTDMA antenna module, a single cellular modem, a single wireless localarea network (WLAN) antenna module (such as a “Wi-Fi” antenna modulecapable of communicating using one or more of the IEEE 802.11 standardsor another standard), a single WiMax (Worldwide Interoperability forMicrowave Access) antenna module, a single satellite antenna module (ora device capable of wirelessly receiving a data message from an orbitingsatellite), a single 3G antenna module, a single 4G antenna module, asingle 5G antenna module, and the like. As another example, an antennamodule 122 may represent a set or array of antenna modules, such asmultiple antenna modules having one or more TDMA antenna modules,cellular modems, Wi-Fi antenna modules, WiMax antenna modules, satelliteantenna modules, 3G antenna modules, 4G antenna modules, and/or 5Gantenna modules.

As shown in FIG. 1 , the antenna modules 122 may be disposed at spacedapart locations along the length of the train 102. For example, thesingle or sets of antenna modules represented by each antenna module 122may be separated from each other along the length of the train 102 suchthat each single antenna module or antenna module set is disposed on adifferent powered or non-powered unit 108, 110, 112, 148, 150, 152 ofthe train 102. The antenna modules 122 may be configured to send data toand receive data from the off-board remote controller interface 104. Forexample, the off-board remote controller interface 104 may include anantenna module 124 that wirelessly communicates the network data from aremote location that is off of the track 106 to the train 102 via one ormore of the antenna modules 122. Alternatively, the antenna modules 122may be connectors or other components that engage a pathway over whichnetwork data is communicated, such as through an Ethernet connection.

The diverse antenna modules 122 enable the train 102 to receive thenetwork data transmitted by the off-board remote controller interface104 at multiple locations along the train 102. Increasing the number oflocations where the network data can be received by the train 102 mayincrease the probability that all, or a substantial portion, of amessage conveyed by the network data is received by the train 102. Forexample, if some antenna modules 122 are temporarily blocked orotherwise unable to receive the network data as the train 102 is movingrelative to the off-board remote controller interface 104, other antennamodules 122 that are not blocked and are able to receive the networkdata may receive the network data. An antenna module 122 receiving dataand command control signals from the off-board device 104 may in turnre-transmit that received data and signals to the appropriate leadcommunication unit 120 of the lead locomotive consist 114, or the leadcommunication unit 160 of the trailing locomotive consist 140. Any datapacket of information received from the off-board remote controllerinterface 104 may include header information or other means ofidentifying which locomotive in which locomotive consist the informationis intended for. Although the lead communication unit 120 on the leadconsist may be the unit that initiates the transmission of data packetsforming a message to the off-board, remote controller interface 104, allof the lead and trailing communication units may be configured toreceive and transmit data packets forming messages. Accordingly, invarious alternative implementations according to this disclosure, acommand control signal providing operational commands for the lead andtrailing locomotives may originate at the remote controller interface104 rather than at the lead powered unit 108 of the lead consist 114.

Each locomotive or powered unit of the train 102 may include a car bodysupported at opposing ends by a plurality of trucks. Each truck may beconfigured to engage the track 106 via a plurality of wheels, and tosupport a frame of the car body. One or more traction motors may beassociated with one or all wheels of a particular truck, and any numberof engines and generators may be mounted to the frame within the carbody to make up the propulsion subsystems 116, 156 on each of thepowered units. The propulsion subsystems 116, 156 of each of the poweredunits may be further interconnected throughout the train 102 along oneor more high voltage power cables in a power sharing arrangement. Energystorage devices (not shown) may also be included for short term or longterm storage of energy generated by the propulsion subsystems or by thetraction motors when the traction motors are operated in a dynamicbraking or generating mode. Energy storage devices may includebatteries, ultra-capacitors, flywheels, fluid accumulators, and otherenergy storage devices with capabilities to store large amounts ofenergy rapidly for short periods of time, or more slowly for longerperiods of time, depending on the needs at any particular time. The DCor AC power provided from the propulsion subsystems 116, 156 or energystorage devices along the power cable may drive AC or DC traction motorsto propel the wheels. Each of the traction motors may also be operatedin a dynamic braking mode as a generator of electric power that may beprovided back to the power cables and/or energy storage devices. Controlover engine operation (e.g., starting, stopping, fueling, exhaustaftertreatment, etc.) and traction motor operation, as well as otherlocomotive controls, may be provided by way of an on-board controller200 and various operational control devices housed within a cabsupported by the frame of the train 102. In some implementations of thisdisclosure, initiation of these controls may be implemented in the cabof the lead powered unit 108 in the lead consist 114 of the train 102.In other alternative implementations, initiation of operational controlsmay be implemented off-board at the remote controller interface 104, orat a powered unit of a trailing consist. As discussed above, the variouscomputer control systems involved in the operation of the train 102 maybe distributed across a number of local and/or remote physical locationsand communicatively coupled over one or more wireless or wiredcommunication networks.

As shown in FIG. 2 , an exemplary implementation of the control system100 may include the on-board controller 200. The on-board controller 200may include an energy management system 232 configured to determine,e.g., one or more of throttle requests, dynamic braking requests, andpneumatic braking requests 234 for one or more of the powered andnon-powered units of the train. The energy management system 232 may beconfigured to make these various requests based on a variety of measuredoperational parameters, track grade, track conditions, freight loads,trip plans, and predetermined maps or other stored data with one or moregoals of improving availability, safety, timeliness, overall fueleconomy and emissions output for individual powered units, consists, orthe entire train. The cab of the lead powered unit 108, 148 in each ofthe consists may also house a plurality of operational control devicesand control system interfaces. The operational control devices may beused by an operator to manually control the locomotive, or may becontrolled electronically via messages received from off-board thetrain. Operational control devices may include, among other things, anengine run/isolation switch, a generator field switch, an automaticbrake handle, an independent brake handle, a lockout device, and anynumber of circuit breakers. Manual input devices may include switches,levers, pedals, wheels, knobs, push-pull devices, touch screen displays,etc.

Operation of the engines, generators, inverters, converters, and otherauxiliary devices may be at least partially controlled by switches orother operational control devices that may be manually movable between arun or activated state and an isolation or deactivated state by anoperator of the train 102. The operational control devices may beadditionally or alternatively activated and deactivated by solenoidactuators or other electrical, electromechanical, or electro-hydraulicdevices. The off-board remote controller interface 104, 204 may alsorequire compliance with security protocols to ensure that onlydesignated personnel may remotely activate or deactivate componentson-board the train from the off-board remote controller interface aftercertain prerequisite conditions have been met. The off-board remotecontroller interface may include various security algorithms or othermeans of comparing an operator authorization input with a predefinedsecurity authorization parameter or level. The security algorithms mayalso establish restrictions or limitations on controls that may beperformed based on the location of a locomotive, authorization of anoperator, and other parameters.

Circuit breakers may be associated with particular components orsubsystems of a locomotive on the train 102, and configured to trip whenoperating parameters associated with the components or subsystemsdeviate from expected or predetermined ranges. For example, circuitbreakers may be associated with power directed to individual tractionmotors, HVAC components, and lighting or other electrical components,circuits, or subsystems. When a power draw greater than an expected drawoccurs, the associated circuit breaker may trip, or switch from a firststate to a second state, to interrupt the corresponding circuit. In someimplementations of this disclosure, a circuit breaker may be associatedwith an on-board control system or communication unit that controlswireless communication with the off-board remote controller interface.After a particular circuit breaker trips, the associated component orsubsystem may be disconnected from the main electrical circuit of thelocomotive 102 and remain nonfunctional until the corresponding breakeris reset. The circuit breakers may be manually tripped or reset.Alternatively or in addition, the circuit breakers may include actuatorsor other control devices that can be selectively energized toautonomously or remotely switch the state of the associated circuitbreakers in response to a corresponding command received from theoff-board remote controller interface 104, 204. In some embodiments, amaintenance signal may be transmitted to the off-board remote controllerinterface 104, 204 upon switching of a circuit breaker from a firststate to a second state, thereby indicating that action such as a resetof the circuit breaker may be needed.

In some situations, train 102 may travel through several differentgeographic regions and encounter different operating conditions in eachregion. For example, different regions may be associated with varyingtrack conditions, steeper or flatter grades, speed restrictions, noiserestrictions, and/or other such conditions. Some operating conditions ina given geographic region may also change over time as, for example,track rails wear and speed and/or noise restrictions are implemented orchanged. Other circumstantial conditions, such as distances betweensidings, distances from rail yards, limitations on access to maintenanceresources, and other such considerations may vary throughout the courseof mission. Operators may therefore wish to implement certain controlparameters in certain geographic regions to address particular operatingconditions.

To help operators implement desired control strategies based on thegeographic location of the train 102, the on-board controller 200 may beconfigured to include a graphical user interface (GUI) that allowsoperators and/or other users to establish and define the parameters ofgeo-fences along a travel route. A geo-fence is a virtual barrier thatmay be set up in a software program and used in conjunction with globalpositioning systems (GPS) or radio frequency identification (RFID) todefine geographical boundaries. As an example, a geo-fence may bedefined along a length of track that has a grade greater than a certainthreshold. A first geo-fence may define a no-stop zone, where the trackgrade is so steep that a train will not be able to traverse the lengthof track encompassed by the first geo-fence if allowed to stop. A secondgeo-fence may define an unfavorable-stop zone, where the grade is steepenough that a train stopping in the unfavorable-stop zone may be able totraverse the second geo-fence after a stop, but will miss a tripobjective such as arriving at a destination by a certain time. A thirdgeo-fence may define a favorable-stop zone, where the grade of the trackis small enough that the train will be able to come to a complete stopwithin the favorable-stop zone for reasons such as repair or adjustmentof various components or subsystems, and then resume travel and traversethe third geo-fence while meeting all trip objectives.

The remote controller interface 104 may include a GUI configured todisplay information and receive user inputs associated with the train.The GUI may be a graphic display tool including menus (e.g., drop-downmenus), modules, buttons, soft keys, toolbars, text boxes, field boxes,windows, and other means to facilitate the conveyance and transfer ofinformation between a user and remote controller interface 104, 204.Access to the GUI may require user authentication, such as, for example,a username, a password, a pin number, an electromagnetic passkey, etc.,to display certain information and/or functionalities of the GUI.

The energy management system 232 of the controller 200 on-board alocomotive 208 may be configured to automatically determine one or moreof throttle requests, dynamic braking requests, and pneumatic brakingrequests 234 for one or more of the powered and non-powered units of thetrain. Alternatively, and in accordance with various embodiments of thisdisclosure, acceleration values for each of the locomotives may bedetermined using independent virtual in-train forces modelling enginesassociated with energy management system 232 onboard each of a pluralityof locomotives in a train. Each of the plurality of locomotives may alsoinclude an analytics engine and a calibration engine configured toassimilate, analyze, and calibrate real time information from otherlocomotives and from sensors disposed on draft gears and couplersinterconnecting the locomotives with determinations made by theindependent virtual in-train forces modelling engine onboard therespective locomotive, with the plurality of locomotives of the trainbeing configured to operate collectively based on a common goal ofminimizing in-train forces without being dependent on a command from alead locomotive or central command. Each of the independent virtualin-train forces modelling engines is configured to use machine learningfor controlling the acceleration values for the locomotive on which itis mounted independently from any command received from offboard thelocomotive. The train control system according to these exemplaryembodiments may include a data acquisition hub communicatively connectedto one or more of sensors and databases associated with one or morelocomotives or other components of a train and configured to acquirereal-time and historical operational and structural data from one ormore of systems and components of the train. The one or more sensorsacquiring real-time data may include one or more of LIDAR sensors, RADARsensors, accelerometers, gyroscopic sensors, optical recognitionsensors, and physical strain gages configured to measure displacementsor forces at the draft gears and couplers interconnecting thelocomotives. Each of the independent virtual in-train forces modellingengines mounted on a respective locomotive may be configured to simulatein-train forces and train operational characteristics usingphysics-based equations, kinematic or dynamic modeling of behavior ofthe train or components of the train during operation when the train isaccelerating or slowing down, and inputs derived from stored historicalcontextual data comprising one or more of a number of locomotives in thetrain, position of the locomotive in the train, age or amount of usageof one or more locomotives of the train or other components of thetrain, weight distribution of the train, length of the train, speed ofthe train, control configurations for one or more locomotives orconsists of the train, power notch settings of one or more locomotivesof the train, braking implemented in the train, positive train controlcharacteristics implemented in the train, grade, topology, temperature,or other characteristics of train tracks on which the train isoperating, and engine operational parameters that affect performance ofone or more locomotive engines for the train. A virtual system modeldatabase may be configured to store one or more virtual system modelssimulated by the independent virtual in-train forces modelling engine,wherein each of the one or more virtual system models includes a mappingbetween different combinations of the stored historical contextual dataand corresponding simulated in-train forces and train operationalcharacteristics that occur when the train is accelerating or slowingdown. The train control system may also include an energy managementsystem 232 associated with each of the plurality of locomotives of thetrain and configured to adjust one or more of throttle requests, dynamicbraking requests, and pneumatic braking requests for each of thelocomotives based at least in part on real time information from otherlocomotives and draft gears and couplers interconnecting the locomotivesassimilated with determinations made by the independent virtual in-trainforces modelling engine onboard the respective locomotive, with theplurality of locomotives of the train being configured to operatecollectively based on a common goal of minimizing in-train forces. Theenergy management system 232 may be configured to make these variousrequests based on a variety of measured operational parameters, trackconditions, freight loads, trip plans, and predetermined maps or otherstored data with a goal of improving one or more of availability,safety, maintenance of in-train forces within desired parameters,timeliness, and overall fuel economy and emissions output for individuallocomotives, consists, or the entire train.

Some of the measured operational parameters such as track grade or othertrack conditions may be associated with one or more predeterminedgeo-fences. The cab of the lead locomotive 208 in each of the consists114, 140 along the train 102 may also house a plurality of inputdevices, operational control devices, and control system interfaces. Theinput devices may be used by an operator to manually control thelocomotive, or the operational control devices may be controlledelectronically via messages received from off-board the train. The inputdevices and operational control devices may include, among other things,an engine run/isolation switch, a generator field switch, an automaticbrake handle (for the entire train and locomotives), an independentbrake handle (for the locomotive only), a lockout device, and any numberof circuit breakers. Manual input devices may include switches, levers,pedals, wheels, knobs, push-pull devices, and touch screen displays. Thecontroller 200 may also include a microprocessor-based locomotivecontrol system 237 having at least one programmable logic controller(PLC), a cab electronics system 238, and an electronic air (pneumatic)brake system 236, all mounted within a cab of the locomotive. The cabelectronics system 238 may comprise at least one integrated displaycomputer configured to receive and display data from the outputs of oneor more of machine gauges, indicators, sensors, and controls. The cabelectronics system 238 may be configured to process and integrate thereceived data, receive command signals from the off-board remotecontroller interface 204, and communicate commands such as throttle,dynamic braking, and pneumatic braking commands 233 to themicroprocessor-based locomotive control system 237.

The microprocessor-based locomotive control system 237 may becommunicatively coupled with the traction motors, engines, generators,braking subsystems, input devices, actuators, circuit breakers, andother devices and hardware used to control operation of variouscomponents and subsystems on the locomotive. In various alternativeimplementations of this disclosure, some operating commands, such asthrottle and dynamic braking commands, may be communicated from the cabelectronics system 238 to the locomotive control system 237, and otheroperating commands, such as braking commands, may be communicated fromthe cab electronics system 238 to a separate electronic air brake system236. One of ordinary skill in the art will recognize that the variousfunctions performed by the locomotive control system 237 and electronicair brake system 236 may be performed by one or more processing modulesor controllers through the use of hardware, software, firmware, orvarious combinations thereof. Examples of the types of controls that maybe performed by the locomotive control system 237 may includeradar-based wheel slip control for improved adhesion, automatic enginestart stop (AESS) for improved fuel economy, control of the lengths oftime at which traction motors are operated at temperatures above apredetermined threshold, control of generators/alternators, control ofinverters/converters, the amount of exhaust gas recirculation (EGR) andother exhaust aftertreatment processes performed based on detectedlevels of certain pollutants, and other controls performed to improvesafety, increase overall fuel economy, reduce overall emission levels,and increase longevity and availability of the locomotives. The at leastone PLC of the locomotive control system 237 may also be configurable toselectively set predetermined ranges or thresholds for monitoringoperating parameters of various subsystems. When a component detectsthat an operating parameter has deviated from the predetermined range,or has crossed a predetermined threshold, a maintenance signal may becommunicated off-board to the remote controller interface 204. The atleast one PLC of the locomotive control system 237 may also beconfigurable to receive one or more command signals indicative of atleast one of a throttle command, a dynamic braking readiness command,and an air brake command 233, and output one or more correspondingcommand control signals configured to at least one of change a throttleposition, activate or deactivate dynamic braking, and apply or release apneumatic brake, respectively.

The cab electronics system 238 may provide integrated computerprocessing and display capabilities on-board the train 102, and may becommunicatively coupled with a plurality of cab gauges, indicators, andsensors, as well as being configured to receive commands from the remotecontroller interface 204. The cab electronics system 238 may beconfigured to process outputs from one or more of the gauges,indicators, and sensors, and supply commands to the locomotive controlsystem 237. In various implementations, the remote controller interface204 may comprise a distributed system of servers, on-board and/oroff-board the train, or a single laptop, hand-held device, or othercomputing device or server with software, encryption capabilities, andInternet access for communicating with the on-board controller 200 ofthe lead locomotive 208 of a lead consist and the lead locomotive 248 ofa trailing consist. Control command signals generated by the cabelectronics system 238 on the lead locomotive 208 of the lead consistmay be communicated to the locomotive control system 237 of the leadlocomotive of the lead consist, and may be communicated in parallel viaa WiFi/cellular modem 250 off-board to the remote controller interface204. The lead communication unit 120 on-board the lead locomotive of thelead consist may include the WiFi/cellular modem 250 and any othercommunication equipment required to modulate and transmit the commandsignals off-board the locomotive and receive command signals on-boardthe locomotive. As shown in FIG. 2 , the remote controller interface 204may relay commands received from the lead locomotive 208 via anotherWiFi/cellular modem 250 to another cab electronics system 238 on-boardthe lead locomotive 248 of the trailing consist.

The control systems and interfaces on-board and off-board the train mayembody single or multiple microprocessors, field programmable gatearrays (FPGAs), digital signal processors (DSPs), programmable logiccontrollers (PLCs), etc., that include means for controlling operationsof the train 102 in response to operator requests, built-in constraints,sensed operational parameters, and/or communicated instructions from theremote controller interface 104, 204. Numerous commercially availablemicroprocessors can be configured to perform the functions of thesecomponents. Various known circuits may be associated with thesecomponents, including power supply circuitry, signal-conditioningcircuitry, actuator driver circuitry (i.e., circuitry poweringsolenoids, motors, or piezo actuators), and communication circuitry.

The locomotives 208, 248 may be outfitted with any number and type ofsensors known in the art for generating signals indicative of associatedoperating parameters. In one example, a locomotive 208, 248 may includea temperature sensor configured to generate a signal indicative of acoolant temperature of an engine on-board the locomotive. Additionallyor alternatively, sensors may include brake temperature sensors, exhaustsensors, fuel level sensors, pressure sensors, knock sensors, reductantlevel or temperature sensors, speed sensors, motion detection sensors,location sensors, or any other sensor known in the art. The signalsgenerated by the sensors may be directed to the cab electronics system238 for further processing and generation of appropriate commands.

Any number and type of warning devices may also be located on-board eachlocomotive, including an audible warning device and/or a visual warningdevice. Warning devices may be used to alert an operator on-board alocomotive of an impending operation, for example startup of theengine(s). Warning devices may be triggered manually from on-board thelocomotive (e.g., in response to movement of a component or operationalcontrol device to the run state) and/or remotely from off-board thelocomotive (e.g., in response to control command signals received fromthe remote controller interface 204.) When triggered from off-board thelocomotive, a corresponding command signal used to initiate operation ofthe warning device may be communicated to the on-board controller 200and the cab electronics system 238.

The on-board controller 200 and the off-board remote controllerinterface 204 may include any means for monitoring, recording, storing,indexing, processing, and/or communicating various operational aspectsof the locomotive 208, 248. These means may include components such as,for example, a memory, one or more data storage devices, a centralprocessing unit, or any other components that may be used to run anapplication. Furthermore, although aspects of the present disclosure maybe described generally as being stored in memory, one skilled in the artwill appreciate that these aspects can be stored on or read fromdifferent types of computer program products or non-transitorycomputer-readable media such as computer chips and secondary storagedevices, including hard disks, floppy disks, optical media, CD-ROM, orother forms of RAM or ROM.

The off-board remote controller interface 204 may be configured toexecute instructions stored on non-transitory computer readable mediumto perform methods of remote control of the locomotive 230. That is, aswill be described in more detail in the following section, on-boardcontrol (manual and/or autonomous control) of some operations of thelocomotive (e.g., operations of traction motors, engine(s), circuitbreakers, etc.) may be selectively overridden by the off-board remotecontroller interface 204.

Remote control of the various powered and non-powered units on the train102 through communication between the on-board cab electronics system238 and the off-board remote controller interface 204 may be facilitatedvia the various communication units 120, 126, 160, 166 spaced along thetrain 102. The communication units may include hardware and/or softwarethat enables sending and receiving of data messages between the poweredunits of the train and the off-board remote controller interfaces. Thedata messages may be sent and received via a direct data link and/or awireless communication link, as desired. The direct data link mayinclude an Ethernet connection, a connected area network (CAN), oranother data link known in the art. The wireless communications mayinclude satellite, cellular, infrared, and any other type of wirelesscommunications that enable the communication units to exchangeinformation between the off-board remote controller interfaces and thevarious components and subsystems of the train 102.

As shown in the exemplary embodiment of FIG. 2 , the cab electronicssystem 238 may be configured to receive the requests 234 after they havebeen processed by a locomotive interface gateway (LIG) 235, which mayalso enable modulation and communication of the requests through aWiFi/cellular modem 250 to the off-board remote controller interface(back office) 204. The cab electronics system 238 may be configured tocommunicate commands (e.g., throttle, dynamic braking, and brakingcommands 233) to the locomotive control system 237 and an electronic airbrake system 236 on-board the lead locomotive 208 in order toautonomously control the movements and/or operations of the leadlocomotive.

In parallel with communicating commands to the locomotive control system237 of the lead locomotive 208, the cab electronics system 238 on-boardthe lead locomotive 208 of the lead consist may also communicatecommands to the off-board remote controller interface 204. The commandsmay be communicated either directly or through the locomotive interfacegateway 235, via the WiFi/cellular modem 250, off-board the leadlocomotive 208 of the lead consist to the remote controller interface204. The remote controller interface 204 may then communicate thecommands received from the lead locomotive 208 to the trailing consistlead locomotive 248. The commands may be received at the trailingconsist lead locomotive 248 via another WiFi/cellular modem 250, andcommunicated either directly or through another locomotive interfacegateway 235 to a cab electronics system 238. The cab electronics system238 on-board the trailing consist lead locomotive 248 may be configuredto communicate the commands received from the lead locomotive 208 of thelead consist to a locomotive control system 237 and an electronic airbrake system 236 on-board the trailing consist lead locomotive 248. Thecommands from the lead locomotive 208 of the lead consist may also becommunicated via the network connection 118 from the trailing consistlead locomotive 248 to one or more trailing powered units 150 of thetrailing consist 140. The result of configuring all of the lead poweredunits of the lead and trailing consists to communicate via the off-boardremote controller interface 204 is that the lead powered unit of eachtrailing consist may respond quickly and in close coordination withcommands responded to by the lead powered unit of the lead consist.Additionally, each of the powered units in various consists along a longtrain may quickly and reliably receive commands such as throttle,dynamic braking, and pneumatic braking commands 234 initiated by a leadlocomotive in a lead consist regardless of location and conditions.

In alternative embodiments according to this disclosure, each of thelocomotives may include a locomotive control system 237 implementingadvanced independent power control theory, such as shown schematicallyin FIGS. 4-6 and 8 , and comprising independent virtual in-train forcesmodelling engines onboard each of the plurality of locomotives in atrain. Each of the plurality of locomotives may include an analyticsengine and a calibration engine configured to assimilate, analyze, andcalibrate real time information from other locomotives and from sensorsassociated with draft gears and couplers interconnecting thelocomotives, such as shown in FIG. 7 , with determinations made by theindependent virtual in-train forces modelling engine onboard therespective locomotive, with the plurality of locomotives of the trainbeing configured to operate collectively based on a common goal ofminimizing in-train forces without being dependent on a command from alead locomotive or central command. Each of the independent virtualin-train forces modelling engines may be configured to generate anintra-consist force model, such as shown in FIGS. 4 and 5 . As shown inFIG. 4 , the independent virtual in-train forces modelling engineonboard each locomotive may generate an intra-consist force model usingphysics-based equations, and kinematic or dynamic modeling of behaviorof the train. The determinations of the model may be corrected based onactual draft gear force measurements at the draft gears and couplersconnected to the front and rear of the locomotive. Buff forces, forexample, the pushing forces from the subject locomotive pushing againsta locomotive connected to the front of the subject locomotive, may bedampened by slowing down the subject locomotive, or reducing theacceleration values for the subject locomotive. Similarly, draft forces,for example, the pulling forces being exerted on the front end draftgear and coupler of the subject locomotive by another locomotiveconnected to the front end of the subject locomotive, may be dampened byspeeding up, or increasing the acceleration values for the subjectlocomotive. The modelling engines may be configured to use machinelearning for controlling the acceleration values for the locomotives onwhich they are mounted independently from any command received fromoffboard the locomotive. The locomotive control systems may becommunicatively coupled with a data acquisition hub, which may becommunicatively connected to one or more of sensors and databasesassociated with one or more locomotives or other components of a trainand configured to acquire real-time and historical operational andstructural data from one or more of systems and components of the train.The one or more sensors acquiring real-time data may include one or moreof LIDAR sensors, RADAR sensors, accelerometers, gyroscopic sensors,optical recognition sensors, and physical strain gages, such as shown inFIG. 7 , and may be configured to measure displacements or forces at thedraft gears and couplers interconnecting the locomotives. Each of theindependent virtual in-train forces modelling engines mounted on arespective locomotive may be configured to simulate in-train forces andtrain operational characteristics using physics-based equations,kinematic or dynamic modeling of behavior of the train or components ofthe train during operation when the train is accelerating or slowingdown, and inputs derived from stored historical contextual datacomprising one or more of a number of locomotives in the train, positionof the locomotive in the train, age or amount of usage of one or morelocomotives of the train or other components of the train, weightdistribution of the train, length of the train, speed of the train,control configurations for one or more locomotives or consists of thetrain, power notch settings of one or more locomotives of the train,braking implemented in the train, positive train control characteristicsimplemented in the train, grade, topology, temperature, or othercharacteristics of train tracks on which the train is operating, andengine operational parameters that affect performance of one or morelocomotive engines for the train. A virtual system model database may beconfigured to store one or more virtual system models simulated by theindependent virtual in-train forces modelling engine, wherein each ofthe one or more virtual system models includes a mapping betweendifferent combinations of the stored historical contextual data andcorresponding simulated in-train forces and train operationalcharacteristics that occur when the train is accelerating or slowingdown. The train control system may also include an energy managementsystem associated with each of the plurality of locomotives of the trainand configured to adjust one or more of throttle requests, dynamicbraking requests, and pneumatic braking requests for each of thelocomotives based at least in part on real time information from otherlocomotives and draft gears and couplers interconnecting the locomotivesassimilated with determinations made by the independent virtual in-trainforces modelling engine onboard the respective locomotive, with theplurality of locomotives of the train being configured to operatecollectively based on a common goal of minimizing in-train forces.

The integrated cab electronics systems 238 on the powered units of thelead consist 114 and on the powered units of the trailing consist 140may also be configured to receive and generate commands for configuringor reconfiguring various switches, handles, and other operationalcontrol devices on-board each of the powered units of the train asrequired before the train begins on a journey, or after a failure occursthat requires reconfiguring of all or some of the powered units.Examples of switches and handles that may require configuring orreconfiguring before a journey or after a failure may include an enginerun switch, a generator field switch, an automatic brake handle, and anindependent brake handle. Remotely controlled actuators on-board thepowered units in association with each of the switches and handles mayenable remote, autonomous configuring and reconfiguring of each of thedevices. For example, before the train begins a journey, or after acritical failure has occurred on one of the lead or trailing poweredunits, commands may be sent from the off-board remote controllerinterface 204 to any powered unit in order to automatically reconfigureall of the switches and handles as required on-board each powered unitwithout requiring an operator to be on-board the train. Following thereconfiguring of all of the various switches and handles on-board eachlocomotive, the remote controller interface may also send messages tothe cab electronics systems on-board each locomotive appropriate forgenerating other operational commands such as changing throttlesettings, activating or deactivating dynamic braking, and applying orreleasing pneumatic brakes. This capability saves the time and expenseof having to delay the train while sending an operator to each of thepowered units on the train to physically switch and reconfigure all ofthe devices required.

FIG. 3 is an illustration of a system according to an exemplaryembodiment of this disclosure for utilizing real-time data forpredictive analysis of the performance of a monitored computer system,such as train control system 100 shown in FIG. 1 . The system 300 mayinclude a series of sensors (i.e., Sensor A 304, Sensor B 306, Sensor C308) interfaced with the various components of a monitored system 302, adata acquisition hub 312, an analytics server 316, and a client device328. The monitored system 302 may include one or more of the traincontrol systems illustrated in FIG. 2 , such as an energy managementsystem, a cab electronics system, and a locomotive control system. Itshould be understood that the monitored system 302 can be anycombination of components whose operations can be monitored with sensorsand where each component interacts with or is related to at least oneother component within the combination. For a monitored system 302 thatis a train control system, the sensors may include brake temperaturesensors, exhaust sensors, fuel level sensors, pressure sensors, stresssensors, knock sensors, reductant level or temperature sensors,generator power output sensors, voltage or current sensors, speedsensors, motion detection sensors, location sensors, wheel temperatureor bearing temperature sensors, or any other sensor known in the art formonitoring various train operational parameters and train componentstructural characteristics.

The sensors are configured to provide output values for systemparameters that indicate the operational status and/or “health” of themonitored system 302. The sensors may include sensors for monitoring theoperational status and/or health of the various physical systemsassociated with operation of a train, as well as the operational statusof the various computer systems and subsystems associated with operationof the train. The sensors may also be configured to measure additionaldata that can affect system operation. For example, sensor output caninclude environmental information, e.g., temperature, humidity, etc.,which can impact the operation and efficiency of the various traincontrol systems.

In one exemplary embodiment, the various sensors 304, 306, 308 may beconfigured to output data in an analog format. For example, electricalpower sensor measurements (e.g., voltage, current, etc.) are sometimesconveyed in an analog format as the measurements may be continuous inboth time and amplitude. In another embodiment, the sensors may beconfigured to output data in a digital format. For example, the sameelectrical power sensor measurements may be taken in discrete timeincrements that are not continuous in time or amplitude. In stillanother embodiment, the sensors may be configured to output data ineither an analog or digital format depending on the samplingrequirements of the monitored system 302.

The sensors can be configured to capture output data at split-secondintervals to effectuate “real time” data capture. For example, in oneembodiment, the sensors can be configured to generate hundreds ofthousands of data readings per second. It should be appreciated,however, that the number of data output readings taken by a sensor maybe set to any value as long as the operational limits of the sensor andthe data processing capabilities of the data acquisition hub 312 are notexceeded.

Each sensor may be communicatively connected to the data acquisition hub312 via an analog or digital data connection 310. The data acquisitionhub 312 may be a standalone unit or integrated within the analyticsserver 316 and can be embodied as a piece of hardware, software, or somecombination thereof. In one embodiment, the data connection 310 is a“hard wired” physical data connection (e.g., serial, network, etc.). Forexample, a serial or parallel cable connection between the sensor andthe hub 312. In another embodiment, the data connection 310 is awireless data connection. For example, a 5G radio frequency (RF)cellular connection, BLUETOOTH.TM., infrared or equivalent connectionbetween the sensor and the hub 312.

The data acquisition hub 312 may be configured to communicate“real-time” data from the monitored system 302 to the analytics server316 using a network connection 314. In one embodiment, the networkconnection 314 is a “hardwired” physical connection. For example, thedata acquisition hub 312 may be communicatively connected (via Category5 (CAT5), fiber optic or equivalent cabling) to a data server (notshown) that is communicatively connected (via CAT5, fiber optic orequivalent cabling) through the Internet and to the analytics server 316server, the analytics server 316 being also communicatively connectedwith the Internet (via CAT5, fiber optic, or equivalent cabling). Inanother embodiment, the network connection 314 is a wireless networkconnection (e.g., 5G cellular, Wi-Fi, WLAN, etc.). For example,utilizing an 802.11a/b/g or equivalent transmission format. In practice,the network connection utilized is dependent upon the particularrequirements of the monitored system 302. Data acquisition hub 312 mayalso be configured to supply warning and alarms signals as well ascontrol signals to monitored system 302 and/or sensors 304, 306, and 308as described in more detail below.

As shown in FIG. 3 , in one embodiment, the analytics server 316 mayhost an analytics engine 318, a virtual system modeling engine 324, acalibration engine 334, and several databases 326, 330, and 332.Additional engines or processing modules may also be included inanalytics server 316, such as an operator behavior modeling engine, asimulation engine, and other machine learning or artificial intelligenceengines or processing modules. The virtual system modeling engine 324can be, e.g., a computer modeling system. In this context, the modelingengine can be used to model in-train forces as described above.Analytics engine 318 can be configured to generate predicted data forthe monitored systems and analyze differences between the predicted dataand the real-time data received from data acquisition hub 312. Analyticsserver 316 may be interfaced with a monitored train control system 302via sensors, e.g., sensors 304, 306, and 308. The various sensors areconfigured to supply real-time data from the various physical componentsand computer systems and subsystems of train 102. The real-time data iscommunicated to analytics server 316 via data acquisition hub 312 andnetwork 314. Hub 312 can be configured to provide real-time data toanalytics server 316 as well as alarming, sensing and control featuredfor the monitored system 302, such as the train control system 100.

The real-time data from data acquisition hub 312 can be passed to acomparison engine, which can be separate from or form part of analyticsengine 318. The comparison engine can be configured to continuouslycompare the real-time data with predicted values generated by virtualsystem modeling engine 324 or another simulation engine included as partof analytics server 316. Based on the comparison, the comparison enginecan be further configured to determine whether deviations between thereal-time values and the expected values exist, and if so to classifythe deviation, e.g., high, marginal, low, etc. The deviation level canthen be communicated to a decision engine, which can also be included aspart of analytics engine 318 or as a separate processing module. Thedecision engine can be configured to look for significant deviationsbetween the predicted values and real-time values as received from thecomparison engine. If significant deviations are detected, the decisionengine can also be configured to determine whether an alarm conditionexists, activate the alarm and communicate the alarm to a Human-MachineInterface (HMI) for display in real-time via, e.g., client 328. Thedecision engine of analytics engine 318 can also be configured toperform root cause analysis for significant deviations in order todetermine the interdependencies and identify any failure relationshipsthat may be occurring. The decision engine can also be configured todetermine health and performance levels and indicate these levels forthe various processes and equipment via the HMI of client 328. All ofwhich, when combined with the analytical and machine learningcapabilities of analytics engine 318 allows the operator to minimize therisk of catastrophic equipment failure by predicting future failures andproviding prompt, informative information concerning potential/predictedfailures before they occur. Avoiding catastrophic failures reduces riskand cost, and maximizes facility performance and up time.

A simulation engine that may be included as part of analytics server 316may operate on complex logical models of the various control systems andsubsystems of on-board controller 200 and train control system 100.These models may be continuously and automatically synchronized with theactual status of the control systems based on the real-time dataprovided by the data acquisition hub 312 to analytics server 316. Inother words, the models are updated based on current switch status,breaker status, e.g., open-closed, equipment on/off status, etc. Thus,the models are automatically updated based on such status, which allowsa simulation engine to produce predicted data based on the current trainoperational status. This in turn, allows accurate and meaningfulcomparisons of the real-time data to the predicted data. Example modelsthat can be maintained and used by analytics server 316 may includemodels used to calculate train trip optimization, determine componentoperational requirements for improved asset life expectancy, determineefficient allocation and utilization of computer control systems andcomputer resources, etc. In certain embodiments, data acquisition hub312 may also be configured to supply equipment identification associatedwith the real-time data. This identification can be cross referencedwith identifications provided in the models.

In one embodiment, if a comparison performed by a comparison engineindicates that a differential between a real-time sensor output valueand an expected value exceeds a threshold value but remains below analarm condition (i.e., alarm threshold value), a calibration request maybe generated by the analytics engine 318. If the differential exceedsthe alarm threshold value, an alarm or notification message may begenerated by the analytics engine 318. The alarm or notification messagemay be sent directly to the client (i.e., user) 328 for display inreal-time on a web browser, pop-up message box, e-mail, or equivalent onthe client 328 display panel. In another embodiment, the alarm ornotification message may be sent to a wireless mobile device to bedisplayed for the user by way of a wireless router or equivalent deviceinterfaced with the analytics server 316. The alarm can be indicative ofa need for a repair event or maintenance, such as synchronization of anycomputer control systems that are no longer communicating withinallowable latency parameters. The responsiveness, calibration, andsynchronization of various computer systems can also be tracked bycomparing expected operational characteristics based on historical dataassociated with the various systems and subsystems of the train toactual characteristics measured after implementation of controlcommands, or by comparing actual measured parameters to predictedparameters under different operating conditions.

Virtual system modeling engine 324 may create multiple models that canbe stored in the virtual system model database 326. Machine learningalgorithms may be employed by virtual system modeling engine 324 tocreate a variety of virtual model applications based on real time andhistorical data gathered by data acquisition hub 314 from a largevariety of sensors measuring operational parameters of train 102. Thevirtual system models may include components for modeling reliability ofvarious train physical systems and distributed computer control systems.In addition, the virtual system models created by virtual systemmodeling engine 324 may include dynamic control logic that permits auser to configure the models by specifying control algorithms and logicblocks in addition to combinations and interconnections of trainoperational components and control systems. Virtual system modeldatabase 326 can be configured to store the virtual system models, andperform what-if simulations. In other words, the database of virtualsystem models can be used to allow a system designer to makehypothetical changes to the train control systems and test the resultingeffect, without having to actually take the train out of service orperform costly and time consuming analysis. Such hypotheticalsimulations performed by virtual systems modeling engine 324 can be usedto learn failure patterns and signatures as well as to test proposedmodifications, upgrades, additions, etc., for the train control system.The real-time data, as well as detected trends and patterns produced byanalytics engine 318 can be stored in real-time data acquisitiondatabases 330 and 332.

As discussed above, the virtual system model may be periodicallycalibrated and synchronized with “real-time” sensor data outputs so thatthe virtual system model provides data output values that are consistentwith the actual “real-time” values received from the sensor outputsignals. Unlike conventional systems that use virtual system modelsprimarily for system design and implementation purposes (i.e., offlinesimulation and facility planning), the virtual system train controlmodels or other virtual computer system models described herein may beupdated and calibrated with the real-time system operational data toprovide better predictive output values. A divergence between thereal-time sensor output values and the predicted output values maygenerate either an alarm condition for the values in question and/or acalibration request that is sent to a calibration engine 334.

A train control system according to an exemplary embodiment of thisdisclosure may use machine learning for controlling the ramp rate atwhich a train accelerates after braking, such as when the train istransitioning from dynamic braking after traveling down a long hill andramping up acceleration as the train then travels up an adjacent hill.The train control system may include data acquisition hub 312communicatively connected to one or more of sensors and databasesassociated with one or more locomotives or other components of the trainand configured to acquire real-time and historical operational andstructural data for use as training data from one or more of systems andcomponents of the train. Virtual system modeling engine 324 may beconfigured to simulate in-train forces and train operationalcharacteristics using physics-based equations, kinematic or dynamicmodeling of behavior of the train or components of the train duringoperation when the train is accelerating or slowing down, and inputsderived from stored historical contextual data. The stored historicalcontextual data may include one or more of a number of locomotives inthe train, age or amount of usage of one or more locomotives of thetrain or other components of the train, weight distribution of thetrain, length of the train, speed of the train, control configurationsfor one or more locomotives or consists of the train, power notchsettings of one or more locomotives of the train, braking implemented inthe train, positive train control characteristics implemented in thetrain, grade, temperature, or other characteristics of train tracks onwhich the train is operating, and engine operational parameters thataffect performance of one or more locomotive engines for the train.

Virtual system model database 326 may be configured to store one or morevirtual system models simulated by virtual system modeling engine 324,wherein each of the one or more virtual system models includes a mappingbetween different combinations of the stored historical contextual dataand corresponding simulated in-train forces and train operationalcharacteristics that occur when the train is accelerating or slowingdown. Real-time and historical operational and structural data acquiredby data acquisition hub 312 for use as training data may include one ormore of structural stresses on one or more knuckles interconnecting oneor more of locomotives and non-powered rail cars of the train, andmeasured vibrations of engine components caused by harmonic nodesencountered while ramping up power output of one or more of thelocomotive engines of the train

A machine learning engine of the train control system may be configuredto calculate relative weights to assign to each of different types ofthe stored historical contextual data of each of the one or more virtualsystem models and assign the relative weights to the stored historicalcontextual data. The machine learning engine may also be configured totrain a learning system using the weighted stored historical contextualdata and the training data to determine a probability of each of the oneor more virtual system models providing an accurate representation ofactual in-train forces and train operational characteristics that occurduring acceleration of the train after braking using a learning functionincluding at least one learning parameter. Training the learning systemmay include providing the weighted stored historical contextual data asan input to the learning function, the learning function beingconfigured to use the at least one learning parameter to generate anoutput based on the input and cause the learning function to generatethe output based on the input. The training may further includecomparing the output to the training data, wherein the training dataincludes data produced by sensors having captured actual information onin-train forces and train operational characteristics duringacceleration of the train after braking. The training may includecomparing the determined probabilities of each of the virtual systemmodels to a predetermined threshold probability level, and initiatingadjustments to one or more of the calculated relative weights assignedto each of the different types of the stored historical contextual dataof each of the one or more virtual system models to improve thedetermined probabilities of each of the virtual system models based onactual information on in-train forces and train operationalcharacteristics acquired from a plurality of different trains operatingunder different conditions. In some embodiments of a machine learningengine according to this disclosure, the machine learning engine mayalso be configurable by a user in order to adjust the relative weightsthat are assigned to each of different types of the stored historicalcontextual data of each of the one or more virtual system models.Additionally, one or more of the predetermined acceptable ranges ofvalues for simulated in-train forces and train operationalcharacteristics may be configurable by the user.

Energy management system 232 may be associated with one or morelocomotives of the train and configured to adjust one or more ofthrottle requests, dynamic braking requests, and pneumatic brakingrequests for the one or more locomotives of the train based at least inpart on one of the virtual system models with the highest probability ofproviding an accurate representation of actual in-train forces and trainoperational characteristics. The adjustments made by energy managementsystem 232 may also be determined such that one or more of the simulatedin-train forces and train operational characteristics fall within apredetermined acceptable range of values.

Analytics engine 318 and virtual system modeling engine 324 may beconfigured to implement pattern/sequence recognition into a real-timedecision loop that, e.g., is enabled by machine learning. The types ofmachine learning implemented by the various engines of analytics server316 may include various approaches to learning and pattern recognition.The machine learning may include the implementation of associativememory, which allows storage, discovery, and retrieval of learnedassociations between extremely large numbers of attributes in real time.At a basic level, an associative memory stores information about howattributes and their respective features occur together. The predictivepower of the associative memory technology comes from its ability tointerpret and analyze these co-occurrences and to produce variousmetrics. Associative memory is built through “experiential” learning inwhich each newly observed state is accumulated in the associative memoryas a basis for interpreting future events. Thus, by observing normalsystem operation over time, and the normal predicted system operationover time, the associative memory is able to learn normal patterns as abasis for identifying non-normal behavior and appropriate responses, andto associate patterns with particular outcomes, contexts or responses.The analytics engine 318 is also better able to understand componentmean time to failure rates through observation and system availabilitycharacteristics. This technology in combination with the virtual systemmodel can present a novel way to digest and comprehend alarms in amanageable and coherent way.

The machine learning algorithms assist in uncovering the patterns andsequencing of alarms to help pinpoint the location and cause of anyactual or impending failures of physical systems or computer systems.Typically, responding to the types of alarms that may be encounteredwhen operating a train is done manually by experts who have gainedfamiliarity with the system through years of experience. However, attimes, the amount of information is so great that an individual cannotrespond fast enough or does not have the necessary expertise. An“intelligent” system employing machine learning algorithms that observehuman operator actions and recommend possible responses could improvetrain operational safety by supporting an existing operator, or evenmanaging the various train control systems autonomously. Currentsimulation approaches for maintaining transient stability andsynchronization between the various train control systems may involvetraditional numerical techniques that typically do not test all possiblescenarios. The problem is further complicated as the numbers ofcomponents and pathways increase. Through the application of the machinelearning algorithms and virtual system modeling according to variousembodiments of this disclosure, by observing simulations of variousoutcomes determined by different train control inputs and operationalparameters, and by comparing them to actual system responses, it may bepossible to improve the simulation process, thereby improving theoverall design of future train control systems.

The virtual system model database 326, as well as databases 330 and 332,can be configured to store one or more virtual system models, virtualsimulation models, and real-time data values, each customized to aparticular system being monitored by the analytics server 316. Thus, theanalytics server 316 can be utilized to monitor more than one traincontrol system or other computer system associated with the train at atime. As depicted herein, the databases 326, 330, and 332 can be hostedon the analytics server 316 and communicatively interfaced with theanalytics engine 318. In other embodiments, databases 326, 330, and 332can be hosted on one or more separate database servers (not shown) thatare communicatively connected to the analytics server 316 in a mannerthat allows the virtual system modeling engine 324 and analytics engine318 to access the databases as needed. In one embodiment, the client 328may modify the virtual system model stored on the virtual system modeldatabase 326 by using a virtual system model development interfaceincluding well-known modeling tools that are separate from the othernetwork interfaces. For example, dedicated software applications thatrun in conjunction with the network interface may allow a client 328 tocreate or modify the virtual system models.

The client 328 may utilize a variety of network interfaces (e.g., webbrowsers) to access, configure, and modify the sensors (e.g.,configuration files, etc.), analytics engine 318 (e.g., configurationfiles, analytics logic, etc.), calibration parameters (e.g.,configuration files, calibration logic, etc.), virtual system modelingengine 324 (e.g., configuration files, simulation parameters, etc.) andvirtual system model of the various train control systems undermanagement (e.g., virtual system model operating parameters andconfiguration files). Correspondingly, data from those variouscomponents of the monitored system 302 can be displayed on a client 328display panel for viewing by a system administrator or equivalent. Asdescribed above, analytics server 316 may be configured to synchronizeand/or calibrate the various train control systems and subsystems in thephysical world with virtual and/or simulated models and report, e.g.,via visual, real-time display, deviations between the two as well assystem health, alarm conditions, predicted failures, etc. In thephysical world, sensors 304, 306, 308 produce real-time data for thevarious train control processes and equipment that make up the monitoredsystem 302. In the virtual world, simulations generated by the virtualsystem modeling engine 324 may provide predicted values, which arecorrelated and synchronized with the real-time data. The real-time datacan then be compared to the predicted values so that differences can bedetected. The significance of these differences can be determined tocharacterize the health status of the various train control systems andsubsystems. The health status can then be communicated to a useron-board the train or off-board at a remote control facility via alarmsand indicators, as well as to client 328, e.g., via web pages.

In some embodiments, as discussed above, the analytics engine 318 mayinclude a machine learning engine. The machine learning engine mayinclude a train control strategy engine configured to receive trainingdata from a data acquisition hub communicatively coupled to one or moresensors associated with one or more locomotives of a train. The trainingdata may include real-time configuration and operational data, and maybe communicated to the data acquisition hub and to the machine learningengine over wireless and/or wired networks. The training data may berelevant to train control operations, including a plurality of firstinput conditions and a plurality of first response maneuvers, outputs,or first actions to be taken by an operator or automatic controller ofthe train associated with the first input conditions. The training datamay include historical operational data acquired by various sensorsassociated with one or more locomotives of the train during one or moreactual train runs. The training data may also include data indicative ofspecific actions taken by a train operator, or autonomous controller, ordirectly or indirectly resulting from actions taken by the trainoperator or autonomous controller, under a large variety of operatingconditions, and on trains with the same or different equipment,different operational characteristics, and different parameters. Themachine learning engine and train control strategy engine may beconfigured to train a learning system using the training data togenerate a second response maneuver or second action to be taken by thetrain operator based on a second input condition.

The response maneuvers generated by the machine learning engine may beintegrated with and implemented by various train control systems andsubsystems, such as the cab electronics system 238, and locomotivecontrol system 237 shown in FIG. 2 . The resultant controls performed bythe various train control systems and subsystems based on outputs fromthe machine learning engine may improve the operation of trains that arebeing operated fully manually, semi-autonomously, or fully autonomouslyby enabling a shared mental model of train operator behavior betweenexperienced human train operators or engineers, less experiencedengineers, and autonomous or semi-autonomous train control systems. Forexample, a learning system according to various embodiments of thisdisclosure can be trained to learn how experienced human engineersrespond to different inputs under various operating conditions, such asduring the automatic implementation of train control commands by tripoptimizer programs, positive train control (PTC) algorithms, andautomatic train operations (ATO), during extreme weather conditions,during emergency conditions caused by other train traffic or equipmentfailure on the train, while approaching and maneuvering in train yards,and under other train operating conditions. The trained learning systemcan then improve train control systems being operated by lessexperienced engineers, semi-autonomously, or fully autonomously toperform operational maneuvers in a manner consistent with how theexperienced human engineers would respond under similar conditions.Unlike existing methods for maneuvering autonomous vehicles, such as byfollowing a control law that optimizes a variable such as a throttlenotch setting at the expense of performing other operational maneuversthat an experienced human engineer would readily understand, the machinelearning engine disclosed herein may allow less experienced trainengineers or autonomously-operated trains to execute maneuvers includingselecting optimum control settings for a particular set of operationalconditions that cannot be reduced to a control law. Train controlsystems that include a machine learning engine configured to encode realhuman engineer behavior into a train control strategy engine may enableless experienced train engineers, or semi-autonomously or fullyautonomously operated trains to perform optimized train handling acrossdifferent terrains, with different trains, and under different operatingconditions.

In some embodiments, the machine learning engine of analytics engine 318may be configured to receive training data including a plurality offirst input conditions and a plurality of first response maneuvers oroutputs associated with the first input conditions. The first inputconditions can represent conditions which, when applied to a trainoperating system or when perceived by a train engineer, lead to aparticular response maneuver or output being performed. A “responsemaneuver” as used herein, refers to any action that may be taken by ahuman engineer or that may directly or indirectly result from an actiontaken by a human engineer. An output may include any actual acquiredreal-time and historical operational and structural data such as dataproduced by sensors having captured actual information on in-trainforces and train operational characteristics. The input conditions caninclude a state or control configuration of a particular locomotive in aconsist, a representation or state of an environment surrounding theconsist, including behavior of other trains or locomotives on the sameor interconnected tracks in the same geographical area, and commands,instructions, or other communications received from other entities.Inputs may also be derived from stored historical contextual datacomprising one or more of a number of locomotives in the train, age oramount of usage of one or more locomotives of the train or othercomponents of the train, weight distribution of the train, length of thetrain, speed of the train, control configurations for one or morelocomotives or consists of the train, power notch settings of one ormore locomotives of the train, braking implemented in the train,positive train control characteristics implemented in the train, grade,temperature, or other characteristics of train tracks on which the trainis operating, and engine operational parameters that affect performanceof one or more locomotive engines for the train.

The first input conditions can include an indication of a maneuvercommand. A maneuver command can be a command, instruction, or otherinformation associated with a maneuver that a locomotive is expected,desired, or required to perform. Maneuver commands can vary inspecificity and may include commands specific to an exact set of tracksalong which the locomotive is required to travel to reach a generalobjective, specific throttle notch settings for one or more lead and/ortrailing locomotives at different locations, under different loads ortrip parameters, braking and dynamic braking commands, and other controlsettings to be implemented by the cab electronics system, throttle,dynamic braking and braking commands, and the locomotive control system.

The machine learning engine may be configured to train a learning systemusing the training data to generate a second response maneuver or outputbased on a second input condition. The machine learning engine canprovide the training data as an input to the learning system, monitor anoutput of the learning system, and modify the learning system based onthe output. The machine learning engine can compare the output to theplurality of first response maneuvers, determine a difference betweenthe output and the plurality of first response maneuvers, and modify thelearning system based on the difference between the output and theplurality of first response maneuvers. For example, the plurality offirst response maneuvers may represent a goal or objective that themachine learning engine is configured to cause the learning system tomatch, by modifying characteristics of the learning system until thedifference between the output and the plurality of first responsemaneuvers is less than a threshold difference. In some embodiments, themachine learning engine can be configured to modify characteristics ofthe learning system to minimize a cost function or optimize some otherobjective function or goal, such as reduced emissions, during aparticular train trip or over a plurality of trips or time periods. Themachine learning engine can group the training data into a first set oftraining data for executing a first learning protocol, and a second setof training data for executing a second learning protocol.

The learning system can include a learning function configured toassociate the plurality of input conditions to the plurality of firstresponse maneuvers, and the learning function can definecharacteristics, such as a plurality of parameters. The machine learningengine can be configured to modify the plurality of parameters todecrease the difference between the output of the learning system (e.g.,the output of the learning function) and the plurality of first responsemaneuvers. Once trained, the learning system can be configured toreceive the second input condition and apply the learning function tothe second input condition to generate the second response maneuver. Insome embodiments, the learning system may include a neural network. Theneural network can include a plurality of layers each including one ormore nodes, such as a first layer (e.g., an input layer), a second layer(e.g., an output layer), and one or more hidden layers. The neuralnetwork can include characteristics such weights and biases associatedwith computations that can be performed between nodes of layers. Themachine learning engine can be configured to train the neural network byproviding the first input conditions to the first layer of the neuralnetwork. The neural network can generate a plurality of first outputsbased on the first input conditions, such as by executing computationsbetween nodes of the layers. The machine learning engine can receive theplurality of first outputs, and modify a characteristic of the neuralnetwork to reduce a difference between the plurality of first outputsand the plurality of first response maneuvers.

In some embodiments, the learning system may include a classificationengine, such as a support vector machine (SVM). The SVM can beconfigured to generate a mapping of first input conditions to firstresponse maneuvers or first outputs. For example, the machine learningengine may be configured to train the SVM to generate one or more rulesconfigured to classify training pairs (e.g., each first input conditionand its corresponding first response maneuver or first output). Theclassification of training pairs can enable the mapping of first inputconditions to first response maneuvers by classifying particular firstresponse maneuvers as corresponding to particular first inputconditions. Once trained, the learning system can generate the secondresponse maneuver based on the second input condition by applying themapping or classification to the second input condition.

Another exemplary classification engine that may be utilized in alearning system according to various implementations of this disclosuremay include a decision tree based algorithm such as Random Forests® orRandom Decision Forests. Decision trees may be used for classification,but also for regression problems. When training a dataset to classify avariable, the idea of a decision tree is to divide the data into smallerdatasets based on a certain feature value until the target variables allfall under one category. To avoid overfitting, variations of decisiontree classifiers such as a Random Forests® classifier or an AdaBoostclassifier may be employed. A Random Forests® classifier fits a numberof decision tree classifiers on various sub-samples of the dataset anduses averaging to improve the predictive accuracy and controlover-fitting. The sub-sample sizes are always the same as the originalinput sample size but the samples of the original data frame are drawnwith replacements (bootstrapping). An AdaBoost classifier begins byfitting a classifier on the original dataset and then fits additionalcopies of the classifier on the same dataset where the weights ofincorrectly classified instances are adjusted such that subsequentclassifier focus more on difficult cases. Yet another exemplaryclassification engine may include a Bayesian estimator such as a naiveBayes classifier, which is a family of probabilistic classifiers basedon applying Bayes theorem with strong (naïve) independence assumptionsbetween the features. A naive Bayes classifier may be trained by afamily of algorithms based on a common principle, such as assuming thatthe value of a particular feature is independent of the value of anyother feature, given the class variable. This type of classifier mayalso be trained effectively using supervised learning, which is amachine learning task of learning a function that maps an input to anoutput based on example input-output pairs. The function is inferredfrom labeled training data consisting of a set of training examples.Each example is a pair consisting of an input object (typically avector) and a desired output value (also called a supervisory signal). Asupervised learning algorithm analyzes the training data and produces aninferred function, which can be used for mapping new examples.

In some embodiments, the learning system may include a Markov decisionprocess engine. The machine learning engine may be configured to trainthe Markov decision process engine to determine a policy based on thetraining data, the policy indicating, representing, or resembling how aparticular locomotive would behave while controlled by an experiencedhuman engineer in response to various input conditions. The machinelearning engine can provide the first input conditions to the Markovdecision process engine as a set or plurality of states (e.g., a set orplurality of finite states). The machine learning engine can provide thefirst response maneuvers to the Markov decision process as a set orplurality of actions (e.g., a set or plurality of finite actions). Themachine learning engine can execute the Markov decision process engineto determine the policy that best represents the relationship betweenthe first input conditions and first response maneuvers. It will beappreciated that in various embodiments, the learning system can includevarious other machine learning engines and algorithms, as well ascombinations of machine learning engines and algorithms, that can beexecuted to determine a relationship between the plurality of firstinput conditions and the plurality of first response maneuvers and thustrain the learning system.

In some implementations of this disclosure, train configuration andoperational data may be provided to the machine learning engine over a5G cellular radio frequency telecommunications network interconnectingmultiple nodes of a distributed computer control system. But alternativeembodiments of the present disclosure may be implemented over a varietyof data communication network environments using software, hardware, ora combination of hardware and software to provide the distributedprocessing functions.

INDUSTRIAL APPLICABILITY

The machine learning engine and virtual system modeling engine of thepresent disclosure may be applicable to any grouping of vehicles such aslocomotives or systems of other powered machines where remote access toparticular functions of the machines may be desirable. System processingassociated with such groupings of vehicles or other machines may behighly distributed as a result of recent advances and cost improvementsin sensing technology and communication of large amounts of operationaland configuration data acquired from sensors associated with thevehicles or other machines. Communication networks such as 5G mobilenetworks allow for increased bandwidths, increased throughput, andfaster data speeds than many existing telecommunication technologies,thereby enabling the interconnection of large numbers of devices onmobile platforms such as vehicles, and the transmission of data fromthose interconnected devices at much faster speeds and with much moreaccuracy than currently available. These improvements in datatransmission capabilities allow for the remote distribution of thevarious computer control systems and subsystems that were traditionallyrestricted to being physically located on the controlled devices, suchas locomotives or other vehicles.

Distributed, remote access to the computerized systems associated withthe vehicles in a train, such as control systems and computer systemsmonitoring the various functions performed by the control systems,enhances operational aspects such as automatic train operation (ATO)when human operators are not present or available at the locomotives,monitoring and maintenance of train equipment, and collection of dataprovided by various sensors and other devices during operation of thelocomotives, which can be used to optimize performance, efficiency,safety, and life expectancy of the equipment. The increased amount ofcommunication of data over wireless networks may also increase the needfor systems and methods to predict or monitor for any transient latencyissues in the exchange of data between various remotely distributedcomputer systems, and maintain synchronization of the distributedsystems. Implementation of the above-discussed machine learning andpattern recognition techniques according to various embodiments of thisdisclosure enables the prediction, early identification, and mitigationof any latency issues during the exchange of data between the variouscomputerized systems and subsystems.

Associative memory is built through “experiential” learning in whicheach newly observed state is accumulated in the associative memory as abasis for interpreting future events. The machine learning algorithmsperformed by analytics engine 318 assist in uncovering the patterns andsequencing of train control procedures under a large variety ofoperating conditions to help pinpoint the location and cause of anyactual or impending failures of physical systems or computer controlsystems. As discussed above, train control systems that include amachine learning engine may also be configured to encode real humanengineer behavior into a train control strategy engine that enables lessexperienced train engineers, or semi-autonomously or fully autonomouslyoperated trains to perform optimized train handling across differentterrains, with different trains, and under different operatingconditions. This approach also allows for easy scalability,extensibility, or customization of train control procedures fordifferent types of trains, different sizes of trains, different loadsbeing carried by the trains, different weather conditions, differentemissions and safety standards depending on geographical location, anddifferent overall train operating goals.

During normal operation, a human operator may be located on-board thelead locomotive 208 and within the cab of the locomotive. The humanoperator may be able to control when an engine or other subsystem of thetrain is started or shut down, which traction motors are used to propelthe locomotive, what switches, handles, and other input devices arereconfigured, and when and what circuit breakers are reset or tripped.The human operator may also be required to monitor multiple gauges,indicators, sensors, and alerts while making determinations on whatcontrols should be initiated. However, there may be times when theoperator is not available to perform these functions, when the operatoris not on-board the locomotive 208, and/or when the operator is notsufficiently trained or alert to perform these functions. In addition,the distributed control systems according to this disclosure facilitateremote access to and availability of the locomotives in a train forauthorized third parties, including providing redundancy and reliabilityof monitoring and control of the locomotives and subsystems on-board thelocomotives.

A method of controlling locomotives in lead and trailing consists of atrain in accordance with various aspects of this disclosure may include,for example, receiving an automatic or manually generated configurationfailure signal at the off-board remote controller interface 204. Theconfiguration failure signal may be indicative of a situation at one ormore of the locomotives in the train requiring configuration orreconfiguration of various operational control devices on-board the oneor more locomotives. Dispatch personnel may then initiate thetransmission of a configuration command signal from a remote client 328,to the analytics engine 318 of the analytics server 316, to the remotecontroller interface 204, and to the one or more locomotives requiringreconfiguration. In this way, all of the locomotives in the lead andtrailing consists of the train may be reconfigured in parallel withoutrequiring an operator on-board the train. The configuration commandssignals, like other messages communicated from the remote controllerinterface 204, may also be transmitted only to a lead locomotive in aconsist, and then communicated over a wired connection such as thenetwork connection 118 to one or more trailing locomotives in theconsist. As discussed above, on-board controls of the locomotives in thetrain may also include the energy management system 232 providing one ormore of throttle, dynamic braking, or braking requests 234 to the cabelectronics system 238. The cab electronics system 238 may process andintegrate these requests along with other outputs from various gaugesand sensors, and commands such as the configuration command that mayhave been received from the off-board remote controller interface 204.The cab electronics system 238 may then communicate commands to theon-board locomotive control system 237. In parallel with these on-boardcommunications, the cab electronics system 238 may communicate commandsvia a WiFi/cellular modem 250 back to the off-board remote controllerinterface 204. In various alternative implementations, the analyticsserver 316 and off-board remote controller interface 204 may furtherprocess the commands received from the lead locomotive 208 of the leadconsist or from a back office command center in order to modify thecommands or otherwise interpret the commands before transmittingcommands to the locomotives. Modification of the commands may be basedon additional information the remote controller interface has acquiredfrom data acquisition hub 312 and one or more sensors located on thelocomotives, or other stored data. The commands transmitted from theremote controller interface 204 by dispatch personnel may be receivedfrom the remote controller interface in parallel at each of thelocomotives of multiple trailing consists.

In addition to throttle, dynamic braking, and braking commands, theremote controller interface 204 may also communicate other commands tothe cab electronics systems of the on-board controllers on one or morelocomotives in multiple consists. These commands may include switching acomponent such as a circuit breaker on-board a locomotive from a firststate, in which the circuit breaker has not tripped, to a second state,in which the circuit breaker has tripped. The circuit breaker may betripped in response to detection that an operating parameter of at leastone component or subsystem of the locomotive has deviated from apredetermined range. When such a deviation occurs, a maintenance signalmay be transmitted from the locomotive to the off-board remotecontroller interface 204. The maintenance signal may be indicative of asubsystem having deviated from the predetermined range as indicated by acircuit breaker having switched from a first state to a second state.The method may further include selectively receiving a command signalfrom the remote controller interface 204 at a control device on-boardthe locomotive, with the command signal causing the control device toautonomously switch the component from the second state back to thefirst state. In the case of a tripped circuit breaker, the command mayresult in resetting the circuit breaker.

The method of remotely controlling the locomotives in various consistsof a train may also include configuring one or more programmable logiccontrollers (PLC) of microprocessor-based locomotive control systems 237on-board one or more locomotives to selectively set predetermined rangesfor operating parameters associated with various components orsubsystems. In one exemplary implementation, a locomotive control system237 may determine that a circuit of a particular subsystem of theassociated locomotive is operating properly when the current flowingthrough the circuit falls within a particular range. A circuit breakermay be associated with the circuit and configured to trip when thecurrent flowing through the circuit deviates from the determined range.In another exemplary implementation, the locomotive control system maydetermine that a particular flow rate of exhaust gas recirculation(EGR), or flow rate of a reductant used in exhaust gas aftertreatment,is required in order to meet particular fuel economy and/or emissionlevels. A valve and/or pump regulating the flow rate of exhaust gasrecirculation and/or reductant may be controlled by the locomotivecontrol system when a level of a particular pollutant deviates from apredetermined range. The predetermined ranges for various operatingparameters may vary from one locomotive to another based on specificcharacteristics associated with each locomotive, including age, model,location, weather conditions, type of propulsion system, fuelefficiency, type of fuel, and the like.

A method of controlling locomotives in lead and trailing consists of atrain in accordance with various aspects of this disclosure may includetransmitting an operating control command from a lead locomotive in alead consist of a train off-board to a remote controller interface. Theremote controller interface may then relay that operating controlcommand to one or more lead locomotives of one or more trailing consistsof the train. In this way, the one or more trailing consists of thetrain may all respond reliably and in parallel with the same controlcommands that are being implemented on-board the lead locomotive of thelead consist. As discussed above, on-board controls of the leadlocomotive of the lead consist in the train may include the energymanagement system or human operator 232 providing one or more ofthrottle, dynamic braking, or braking requests 234 to the cabelectronics system 238. The cab electronics system 238 may process andintegrate these requests along with other outputs from various gaugesand sensors, and commands that may have been received from the off-boardremote controller interface 204. The commands received from theoff-board remote controller interface 204 may include commands generatedmanually by a user with the proper permission selecting a particularride-through control level, or automatically based on a particulargeo-fence that a locomotive is entering. The cab electronics system 238may then communicate commands to the on-board locomotive control system237. In parallel with these on-board communications, the cab electronicssystem 238 may communicate the same commands via a WiFi/cellular modem250, or via a locomotive interface gateway 335 and WiFi/cellular modem250 to the off-board remote controller interface 204. In variousalternative implementations, the off-board remote controller interface204 may further process the commands received from the lead locomotive208 of the lead consist in order to modify the commands beforetransmitting the commands to lead locomotives of trailing consists.Modification of the commands may be based on additional information theremote controller interface has acquired from the lead locomotives ofthe trailing consists, trip plans, information from maps or other storeddata, and the results of machine learning, virtual system modeling,synchronization, and calibration performed by the analytics server 316.The commands may be received from the remote controller interface inparallel at each of the lead locomotives 248 of multiple trailingconsists.

A method of controlling locomotives in lead and trailing consists of atrain in accordance with various aspects of this disclosure may includereceiving real-time and historical operational and structural data foruse as training data from one or more of systems and components of thetrain at a data acquisition hub communicatively connected to one or moreof sensors and databases associated with one or more locomotives orother components of the train. The method may further includesimulating, using a virtual system modeling engine, in-train forces andtrain operational characteristics using physics-based equations,kinematic or dynamic modeling of behavior of the train or components ofthe train during operation when the train is accelerating or slowingdown, and inputs derived from stored historical contextual data. Thestored historical contextual data may include one or more of a number oflocomotives in the train, age or amount of usage of one or morelocomotives of the train or other components of the train, weightdistribution of the train, length of the train, speed of the train,control configurations for one or more locomotives or consists of thetrain, power notch settings of one or more locomotives of the train,braking implemented in the train, positive train control characteristicsimplemented in the train, grade, temperature, or other characteristicsof train tracks on which the train is operating, and engine operationalparameters that affect performance of one or more locomotive engines forthe train.

The exemplary method according to this disclosure may still furtherinclude storing one or more virtual system models simulated by thevirtual system modeling engine in a virtual system model database,wherein each of the one or more virtual system models includes a mappingbetween different combinations of the stored historical contextual dataand corresponding simulated in-train forces and train operationalcharacteristics that occur when the train is accelerating or slowingdown. A machine learning engine may calculate relative weights to assignto each of different types of the stored historical contextual data ofeach of the one or more virtual system models and assign the relativeweights to the stored historical contextual data. The machine learningengine may also train a learning system using the weighted storedhistorical contextual data and the training data to determine aprobability of each of the one or more virtual system models providingan accurate representation of actual in-train forces and trainoperational characteristics that occur during acceleration of the trainafter braking. The machine learning engine may use a learning functionincluding at least one learning parameter.

Training the learning system may include providing the weighted storedhistorical contextual data as an input to the learning function, thelearning function being configured to use the at least one learningparameter to generate an output based on the input. The training maycause the learning function to generate the output based on the input.The training may also include comparing the output to the training data,wherein the training data includes data produced by sensors havingcaptured actual information on in-train forces and train operationalcharacteristics during acceleration of the train after braking.Additional aspects of the training may also include comparing thedetermined probabilities of each of the virtual system models to apredetermined threshold probability level, and initiating adjustments toone or more of the calculated relative weights assigned to each of thedifferent types of the stored historical contextual data of each of theone or more virtual system models to improve the determinedprobabilities of each of the virtual system models based on actualinformation on in-train forces and train operational characteristicsacquired from a plurality of different trains operating under differentconditions.

The method of controlling locomotives in lead and trailing consists of atrain in accordance with various aspects of this disclosure may stillfurther include adjusting one or more of throttle requests, dynamicbraking requests, and pneumatic braking requests for the one or morelocomotives of the train using an energy management system associatedwith the one or more locomotives of the train based at least in part onone of the virtual system models with the highest probability ofproviding an accurate representation of actual in-train forces and trainoperational characteristics and with one or more of the simulatedin-train forces and train operational characteristics falling within apredetermined acceptable range of values.

The method of remotely controlling the locomotives in various consistsof a train may also include configuring one or more programmable logiccontrollers (PLC) of microprocessor-based locomotive control systems 237on-board one or more lead locomotives to selectively set predeterminedranges for operating parameters associated with various components orsubsystems. As discussed above, the predetermined ranges for operatingparameters may be selectively set based at least in part on a manuallyor automatically selected ride-through control level and a geo-fenceassociated with the location of the locomotive. In one exemplaryimplementation, a locomotive control system 237 may determine that acircuit of a particular subsystem of the associated locomotive isoperating properly when the current flowing through the circuit fallswithin a particular range. A circuit breaker may be associated with thecircuit and configured to trip when the current flowing through thecircuit deviates from the determined range. In another exemplaryimplementation, the locomotive control system may determine that aparticular flow rate of exhaust gas recirculation (EGR), or flow rate ofa reductant used in exhaust gas aftertreatment, is required in order tomeet particular fuel economy and/or emission levels. A valve and/or pumpregulating the flow rate of exhaust gas recirculation and/or reductantmay be controlled by the locomotive control system when a level of aparticular pollutant deviates from a predetermined range. Thepredetermined ranges for various operating parameters may vary from onelocomotive to another based on specific characteristics associated witheach locomotive, including age, model, location, weather conditions,type of propulsion system, fuel efficiency, type of fuel, and the like.

The method of controlling locomotives in a train in accordance withvarious implementations of this disclosure may still further include thecab electronics system 238 on-board a locomotive receiving andprocessing data outputs from one or more of gauges, indicators, sensors,and controls on-board the locomotive. The cab electronics system 238 mayalso receive and process, e.g., throttle, dynamic braking, and pneumaticbraking requests from the energy management system and/or human operator232 on-board the locomotive, and command signals from the off-boardremote controller interface 204. The command signals received fromoff-board the locomotive, or generated on-board the locomotive may bedetermined at least in part by a selected ride-through control level andthe particular geo-fence associated with the current location of thetrain. The cab electronics system 238 may then communicate appropriatecommands to the locomotive control system 237 and/or electronic airbrake system 236 based on the requests, data outputs and commandsignals. The locomotive control system 237 may perform various controloperations such as resetting circuit breakers, adjusting throttlesettings, activating dynamic braking, and activating pneumatic brakingin accordance with the commands received from the cab electronics system238.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the systems and methods ofthe present disclosure without departing from the scope of thedisclosure. Other embodiments will be apparent to those skilled in theart from consideration of the specification and practice of the systemdisclosed herein. It is intended that the specification and examples beconsidered as exemplary only, with a true scope of the disclosure beingindicated by the following claims and their equivalents.

What is claimed is:
 1. A train control system including a plurality ofindependent virtual in-train forces modelling engines onboard each of aplurality of locomotives in a train, the train control systemcomprising: a data acquisition hub communicatively connected to one ormore of sensors and databases associated with one or more of thelocomotives or other components of the train and configured to acquirereal-time and historical operational and structural data from one ormore of systems and components of the train; an analytics engineconfigured to assimilate and analyze real time information from otherlocomotives and from components such as draft gears and couplersinterconnecting the locomotives with determinations of accelerationvalues for each respective locomotive made by the independent virtualin-train forces modelling engine onboard the respective locomotive, withthe plurality of locomotives of the train being configured to operatecollectively and coordinate respective, acceleration valuesindependently determined on each of the locomotives based on a commongoal for all of the locomotives of minimizing in-train forces withoutbeing dependent on a command from a lead locomotive or central command;each of the plurality of independent virtual in-train forces modellingengines being associated with a machine learning engine and beingdisposed onboard a respective one of the plurality of locomotives andconfigured to simulate in-train forces and train operationalcharacteristics using physics-based equations, kinematic or dynamicmodeling of behavior of the train or components of the train duringoperation when the train is accelerating or slowing down, and inputsderived from stored historical contextual data comprising one or more ofa number of locomotives in the train, age or amount of usage of one ormore locomotives of the train or other components of the train, weightdistribution of the train, length of the train, speed of the train,control configurations for one or more locomotives or consists of thetrain, power notch settings of one or more locomotives of the train,braking implemented in the train, grade, temperature, or othercharacteristics of train tracks on which the train is operating, andengine operational parameters that affect performance of one or morelocomotive engines for the train; a virtual system model databaseconfigured to store one or more virtual system models simulated by theindependent virtual in-train forces modelling engines, wherein each ofthe one or more virtual system models includes a mapping betweendifferent combinations of the stored historical contextual data andcorresponding simulated in-train forces and train operationalcharacteristics that occur when the train is accelerating or slowingdown; and an energy management system associated with each of thelocomotives of the train and configured to adjust one or more ofthrottle requests, dynamic braking requests, and pneumatic brakingrequests for the one or more locomotives of the train based at least inpart on the virtual system model.
 2. The train control system of claim1, wherein the one or more sensors acquiring real-time data include oneor more of LIDAR sensors, RADAR sensors, accelerometers, gyroscopicsensors, optical recognition sensors, and physical strain gaugesconfigured to measure displacements or forces at the draft gears andcouplers interconnecting the locomotives, and the analytics engine isconfigured to integrate the data acquired by each of the sensors toproduce more consistent and accurate force measurements for in-trainforces than would be possible with any one of the sensors by itself. 3.The train control system of claim 2, wherein the machine learning engineincludes at least one of a neural network, a support vector machine, aMarkov decision process engine, a decision tree based algorithm, or aBayesian based estimator.
 4. The train control system of claim 3,wherein the machine learning engine includes a neural network, and themachine learning engine is configured to train the neural network byproviding the inputs derived from stored historical contextual data asthe input to a first layer of the neural network, wherein the outputgenerated by a learning function of the machine learning engine includesa plurality of first outputs from the neural network generated based onthe inputs, and the at least one learning parameter includes acharacteristic of the neural network which is modified to reduce adifference between the plurality of first outputs and the trainingdata..
 5. The train control system of claim 1, wherein the virtualsystem modeling engine is configured to simulate the in-train forces andtrain operational characteristics during a period of time when the trainincludes at least one locomotive with an associated energy managementsystem that is transitioning from a braking control to an accelerationcontrol.
 6. The train control system of claim 1, wherein the real-timeand historical operational and structural data acquired by the dataacquisition hub for use as training data includes one or more ofstructural stresses on one or more couplers or draft gearsinterconnecting one or more of locomotives or locomotives andnon-powered rail cars of the train.
 7. The train control system of claim6, wherein the energy management system is configured to adjust one ormore of throttle requests, dynamic braking requests, and pneumaticbraking requests for the one or more associated locomotives of the trainusing a microprocessor-based locomotive control system, a cabelectronics system, and an electronic pneumatic brake system mountedwithin a cab of each of the one or more locomotives.
 8. The traincontrol system of claim 7, wherein the energy management system isconfigured to adjust one or more of throttle requests, dynamic brakingrequests, and pneumatic braking requests for the one or more associatedlocomotives of the train while transitioning and ramping up from heavydynamic braking at the bottom of a hill to full throttle on the way backup an adjacent hill in a direction of travel of the train along thetrain tracks, and while increasing a ramp rate and maintaining thestructural stresses on one or more of the couplers or draft gears. 9.The train control system of claim 1, wherein the machine learning engineis configurable by a user in order to adjust the relative weights thatare assigned to each of different types of the stored historicalcontextual data of each of the one or more virtual system models, andwherein one or more of the predetermined acceptable ranges of values forsimulated in-train forces and train operational characteristics areconfigurable by the user.
 10. A method of using independent virtualin-train forces modelling engines onboard each of a plurality oflocomotives in a train in order to coordinate the accelerations of eachof the locomotives determined independently onboard each of thelocomotives to minimize in-train forces without being dependent on acommand from a lead locomotive or central command, the methodcomprising: assimilating, analyzing, and calibrating real timeinformation from other locomotives and draft gears and couplersinterconnecting the locomotives with determinations made by theindependent virtual in-train forces modelling engine onboard therespective locomotive; and operating the plurality of locomotives of thetrain collectively based on a common goal of minimizing in-train forceswithout being dependent on a command from a lead locomotive or centralcommand; using machine learning performed by a machine learning engineassociated with each of the independent virtual in-train forcesmodelling engines for controlling the acceleration values for thelocomotive on which it is mounted independently from any commandreceived from offboard the locomotive; acquiring real-time andhistorical operational and structural data from one or more of systemsand components of the train at a data acquisition hub communicativelyconnected to one or more of sensors and databases associated with one ormore locomotives or other components of the train, wherein the one ormore sensors acquiring real-time data may include one or more of LIDARsensors, RADAR sensors, accelerometers, gyroscopic sensors, opticalrecognition sensors, and physical strain gages configured to measuredisplacements or forces at the draft gears and couplers interconnectingthe locomotives; simulating in-train forces and train operationalcharacteristics using each of the independent virtual in-train forcesmodelling engines mounted on a respective locomotive based onphysics-based equations, kinematic or dynamic modeling of behavior ofthe train or components of the train during operation when the train isaccelerating or slowing down, and inputs derived from stored historicalcontextual data comprising one or more of a number of locomotives in thetrain, position of the locomotive in the train, age or amount of usageof one or more locomotives of the train or other components of thetrain, weight distribution of the train, length of the train, speed ofthe train, control configurations for one or more locomotives orconsists of the train, power notch settings of one or more locomotivesof the train, braking implemented in the train, positive train controlcharacteristics implemented in the train, grade, topology, temperature,or other characteristics of train tracks on which the train isoperating, and engine operational parameters that affect performance ofone or more locomotive engines for the train; storing one or morevirtual system models simulated by the independent virtual in-trainforces modelling engine in a virtual system model database, with each ofthe one or more virtual system models including a mapping betweendifferent combinations of the stored historical contextual data andcorresponding simulated in-train forces and train operationalcharacteristics that occur when the train is accelerating or slowingdown; and adjusting one or more of throttle requests, dynamic brakingrequests, and pneumatic braking requests for each of the locomotivesusing an energy management system onboard each of the locomotives of thetrain based at least in part on real time information from otherlocomotives and draft gears and couplers interconnecting the locomotivesassimilated with determinations made by the independent virtual in-trainforces modelling engine onboard the respective locomotive, with theplurality of locomotives of the train operating collectively based on acommon goal of minimizing in-train forces.
 11. The method of claim 10,wherein the machine learning engine includes at least one of a neuralnetwork, a support vector machine, a Markov decision process engine, adecision tree based algorithm, or a Bayesian based estimator.
 12. Themethod of claim 11, wherein the machine learning engine includes aneural network, and the machine learning engine is configured to trainthe neural network by providing the inputs derived from storedhistorical contextual data as the input to a first layer of the neuralnetwork, wherein the output generated by a learning function of themachine learning engine includes a plurality of first outputs from theneural network generated based on the inputs, and the at least onelearning parameter includes a characteristic of the neural network whichis modified to reduce a difference between the plurality of firstoutputs and the training data.
 13. The method of claim 10, wherein thevirtual system modeling engine is configured to simulate the in-trainforces and train operational characteristics during a period of timewhen the train includes at least one locomotive with an associatedenergy management system that is transitioning from a braking control toan acceleration control.
 14. The method of claim 13, wherein the virtualsystem modeling engine is configured to simulate the in-train forces andtrain operational characteristics during a period of time when the trainincludes at least one locomotive with an associated energy managementsystem that is transitioning and ramping up from heavy dynamic brakingat the bottom of a hill to full throttle on the way back up an adjacenthill in a direction of travel of the train along the train tracks. 15.The method of claim 10, wherein the real-time and historical operationaland structural data acquired by the data acquisition hub for use astraining data includes one or more of structural stresses on one or moredraft gears or couplers interconnecting one or more of locomotives andnon-powered rail cars of the train, and measured vibrations of enginecomponents caused by harmonic nodes encountered while ramping up poweroutput of one or more of the locomotive engines of the train.
 16. Themethod of claim 15, wherein the energy management system is configuredto adjust one or more of throttle requests, dynamic braking requests,and pneumatic braking requests for the one or more associatedlocomotives of the train using a microprocessor-based locomotive controlsystem, a cab electronics system, and an electronic pneumatic brakesystem mounted within a cab of each of the one or more locomotives. 17.The method of claim 16, wherein the energy management system isconfigured to adjust one or more of throttle requests, dynamic brakingrequests, and pneumatic braking requests for the one or more associatedlocomotives of the train while transitioning and ramping up from heavydynamic braking at the bottom of a hill to full throttle on the way backup an adjacent hill in a direction of travel of the train along thetrain tracks, and while increasing a ramp rate and maintaining thestructural stresses on one or more knuckles and the vibrations of enginecomponents within predetermined acceptable ranges of values.
 18. Themethod of claim 10, wherein the machine learning engine is configurableby a user in order to adjust the relative weights that are assigned toeach of different types of the stored historical contextual data of eachof the one or more virtual system models, and wherein the predeterminedacceptable ranges of values for simulated in-train forces and trainoperational characteristics are configurable by the user.
 19. Alocomotive control system, comprising: a learning system configured to:receive real-time and historical operational and structural data for useas training data from one or more systems or components of the train ata data acquisition hub communicatively connected to one or more ofsensors and databases associated with one or more locomotives or othercomponents of a train; simulate, using a virtual system modeling engine,in-train forces and train operational characteristics usingphysics-based equations, kinematic or dynamic modeling of behavior ofthe train or components of the train during operation when the train isaccelerating or slowing down, and inputs derived from stored historicalcontextual data comprising one or more of a number of locomotives in thetrain, age or amount of usage of one or more locomotives of the train orother components of the train, weight distribution of the train, lengthof the train, speed of the train, control configurations for one or morelocomotives or consists of the train, power notch settings of one ormore locomotives of the train, braking implemented in the train,positive train control characteristics implemented in the train, grade,temperature, or other characteristics of train tracks on which the trainis operating, and engine operational parameters that affect performanceof one or more locomotive engines for the train; store one or morevirtual system models simulated by the virtual system modeling engine ina virtual system model database, wherein each of the one or more virtualsystem models includes a mapping between different combinations of thestored historical contextual data and corresponding simulated in-trainforces and train operational characteristics that occur when the trainis accelerating or slowing down; calculate, using a machine learningengine, relative weights to assign to each of different types of thestored historical contextual data of each of the one or more virtualsystem models and assigning the relative weights to the storedhistorical contextual data; train the learning system with the machinelearning engine using the weighted stored historical contextual data andthe training data to determine a probability of each of the one or morevirtual system models providing an accurate representation of actualin-train forces and train operational characteristics that occur duringacceleration of the train after braking, and using a learning functionincluding at least one learning parameter, wherein training the learningsystem includes: providing the weighted stored historical contextualdata as an input to the learning function, the learning function beingconfigured to use the at least one learning parameter to generate anoutput based on the input; causing the learning function to generate theoutput based on the input; comparing the output to the training data,wherein the training data includes data produced by sensors havingcaptured actual information on in-train forces and train operationalcharacteristics during acceleration of the train after braking;comparing the determined probabilities of each of the virtual systemmodels to a predetermined threshold probability level; and initiatingadjustments to one or more of the calculated relative weights assignedto each of the different types of the stored historical contextual dataof each of the one or more virtual system models to improve thedetermined probabilities of each of the virtual system models based onactual information on in-train forces and train operationalcharacteristics acquired from a plurality of different trains operatingunder different conditions; and adjust one or more of throttle requests,dynamic braking requests, and pneumatic braking requests for the one ormore locomotives of the train using an energy management systemassociated with the one or more locomotives of the train based at leastin part on one of the virtual system models with the highest probabilityof providing an accurate representation of actual in-train forces andtrain operational characteristics and with one or more of the simulatedin-train forces and train operational characteristics falling within apredetermined acceptable range of values.
 20. The locomotive controlsystem of claim 19, wherein the training data includes configuration andoperational data associated with the inputs derived from storedhistorical contextual data, the training data being generated by one ormore systems or components of the train while the train is beingoperated by an experienced train operator, wherein the output generatedby the learning function represents a goal or objective that the machinelearning engine is configured to cause the learning system to match bymodifying the at least one learning parameter until the differencebetween the output and the training data is less than a predeterminedthreshold difference.